Systems and methods for mobile image capture and processing

ABSTRACT

In various embodiments, methods, systems, and computer program products for processing digital images captured by a mobile device are disclosed. Myriad features enable and/or facilitate processing of such digital images using a mobile device that would otherwise be technically impossible or impractical, and furthermore address unique challenges presented by images captured using a camera rather than a traditional flat-bed scanner, paper-feed scanner or multifunction peripheral.

RELATED APPLICATIONS

This application is related to, and claims the benefit of priority fromU.S. Provisional Application No. 61/586,062 filed Jan. 12, 2012, andfrom U.S. Provisional Application No. 61/720,958 filed Oct. 31, 2012,which are herein incorporated by reference.

FIELD OF INVENTION

The present invention relates to image capture and image processing, andmore particularly to capturing and processing digital images using amobile device.

BACKGROUND OF THE INVENTION

Digital images having depicted therein a document such as a letter, acheck, a bill, an invoice, etc. have conventionally been captured andprocessed using a scanner or multifunction peripheral coupled to acomputer workstation such as a laptop or desktop computer. Methods andsystems capable of performing such capture and processing are well knownin the art and well adapted to the tasks for which they are employed.

However, in an era where day-to-day activities, computing, and businessare increasingly performed using mobile devices, it would be greatlybeneficial to provide analogous document capture and processing systemsand methods for deployment and use on mobile platforms, such as smartphones, digital cameras, tablet computers, etc.

A major challenge in transitioning conventional document capture andprocessing techniques is the limited processing power and imageresolution achievable using hardware currently available in mobiledevices. These limitations present a significant challenge because it isimpossible or impractical to process images captured at resolutionstypically much lower than achievable by a conventional scanner. As aresult, conventional scanner-based processing algorithms typicallyperform poorly on digital images captured using a mobile device.

In addition, the limited processing and memory available on mobiledevices makes conventional image processing algorithms employed forscanners prohibitively expensive in terms of computational cost.Attempting to process a conventional scanner-based image processingalgorithm takes far too much time to be a practical application onmodern mobile platforms.

A still further challenge is presented by the nature of mobile capturecomponents (e.g. cameras on mobile phones, tablets, etc.). Whereconventional scanners are capable of faithfully representing thephysical document in a digital image, critically maintaining aspectratio, dimensions, and shape of the physical document in the digitalimage, mobile capture components are frequently incapable of producingsuch results.

Specifically, images of documents captured by a camera present a newline of processing issues not encountered when dealing with imagescaptured by a scanner. This is in part due to the inherent differencesin the way the document image is acquired, as well as the way thedevices are constructed. The way that some scanners work is to use atransport mechanism that creates a relative movement between paper and alinear array of sensors. These sensors create pixel values of thedocument as it moves by, and the sequence of these captured pixel valuesforms an image. Accordingly, there is generally a horizontal or verticalconsistency up to the noise in the sensor itself, and it is the samesensor that provides all the pixels in the line.

In contrast, cameras have many more sensors in a nonlinear array, e.g.,typically arranged in a rectangle. Thus, all of these individual sensorsare independent, and render image data that is not typically ofhorizontal or vertical consistency. In addition, cameras introduce aprojective effect that is a function of the angle at which the pictureis taken. For example, with a linear array like in a scanner, even ifthe transport of the paper is not perfectly orthogonal to the alignmentof sensors and some skew is introduced, there is no projective effectlike in a camera. Additionally, with camera capture, nonlineardistortions may be introduced because of the camera optics.

In view of the challenges presented above, it would be beneficial toprovide an image capture and processing algorithm and applicationsthereof that compensate for and/or correct problems associated withimage capture and processing using a mobile device, while maintaining alow computational cost via efficient processing methods.

SUMMARY OF THE INVENTION

In one embodiment, a method includes dividing, using a processor, atetragon comprising a digital representation of a document in a digitalimage into a plurality of sections, each section comprising a pluralityof pixels; for each section: determining whether the section containsone or more sharp pixel-to-pixel transitions in a first direction;counting a total number of first direction sharp pixel-to-pixeltransitions for the section (S_(S1)); determining whether the sectioncontains one or more blurred pixel-to-pixel transitions in the firstdirection; counting a total number of first-direction blurredpixel-to-pixel transitions for the section (S_(B1)); determining whetherthe section contains one or more sharp pixel-to-pixel transitions in asecond direction; counting a total number of second-direction sharppixel-to-pixel transitions for the section (S_(S2)); determining whetherthe section contains one or more blurred pixel-to-pixel transitions inthe second direction; counting a total number of second-directionblurred pixel-to-pixel transitions for the section (S_(B2)); determiningthe section is blank upon determining: S_(S1) is less than apredetermined sharp transition threshold, S_(B1) is less than apredetermined blurred transition threshold, S_(S2) is less than apredetermined sharp transition threshold, and S_(B2) is less than apredetermined blurred transition threshold; and determining, for allnon-blank sections, a first direction blur ratio r₁=S_(S1)/S_(B1);determining, for all non-blank sections, a second direction blur ratior₂=S_(S2)/S_(B2); determining that a non-blank section is blurred in thefirst direction upon determining that r₁ is less than a predefinedsection blur ratio threshold; and determining that a non-blank sectionis blurred in the second direction upon determining that r₂ is less thanthe predefined section blur ratio threshold; and determining that anon-blank section is blurred upon determining one or more of: thesection is blurred in the first direction, and the section is blurred inthe section direction; and determining a total number of blurredsections; calculating an image blur ratio R comprising: the total numberblurred sections to a total number of sections; and determining thedigital image is blurred upon determining the image blur ratio isgreater than a predetermined image blur threshold.

In another embodiment, a system includes a processor configured toexecute logic; logic for dividing, using a processor, a tetragoncomprising a digital representation of a document in a digital imageinto a plurality of sections, each section comprising a plurality ofpixels; logic for determining whether the section contains one or moresharp pixel-to-pixel transitions in a first direction; logic forcounting a total number of first direction sharp pixel-to-pixeltransitions for the section (S_(S1)); logic for determining whether thesection contains one or more blurred pixel-to-pixel transitions in thefirst direction; logic for counting a total number of first-directionblurred pixel-to-pixel transitions for the section (S_(B1)); logic fordetermining whether the section contains one or more sharppixel-to-pixel transitions in a second direction; logic for counting atotal number of second direction sharp pixel-to-pixel transitions forthe section (S_(S2)); logic for determining whether the section containsone or more blurred pixel-to-pixel transitions in the second direction;logic for counting a total number of second-direction blurredpixel-to-pixel transitions for the section (S_(B2)); logic fordetermining the section is blank upon determining: S_(S1) is less than apredetermined sharp transition threshold, S_(B1) is less than apredetermined blurred transition threshold, S_(S2) is less than apredetermined sharp transition threshold, and S_(B2) is less than apredetermined blurred transition threshold; and logic for determining,for all non-blank sections, a first direction blur ratior₁=S_(S1)/S_(B1); logic for determining, for all non-blank sections, asecond direction blur ratio r₂=S_(S2)/S_(B2); logic for determining thata non-blank section is blurred in the first direction upon determiningthat r₁ is less than a predefined section blur ratio threshold; andlogic for determining that a non-blank section is blurred in the seconddirection upon determining that r₂ is less than the predefined sectionblur ratio threshold; and logic for determining that a non-blank sectionis blurred upon determining one or more of: the section is blurred inthe first direction; and the section is blurred in the sectiondirection; and logic for determining a total number of blurred sections;logic for calculating an image blur ratio R comprising: the total numberblurred sections to a total number of sections; and logic fordetermining the digital image is blurred upon determining the image blurratio is greater than a predetermined image blur threshold.

In yet another embodiment, a computer program product includes acomputer readable storage medium having computer readable program codestored thereon, the computer readable program code including computerreadable program code configured to divide, use a processor, a tetragoncomprise a digital representation of a document in a digital image intoa plurality of sections, each section comprise a plurality of pixels;computer readable program code configured to determine whether thesection contains one or more sharp pixel-to-pixel transitions in a firstdirection; computer readable program code configured to count a totalnumber of first direction sharp pixel-to-pixel transitions for thesection (S_(S1)); computer readable program code configured to determinewhether the section contains one or more blurred pixel-to-pixeltransitions in the first direction; computer readable program codeconfigured to count a total number of first-direction blurredpixel-to-pixel transitions for the section (S_(B1)); computer readableprogram code configured to determine whether the section contains one ormore sharp pixel-to-pixel transitions in a second direction; computerreadable program code configured to count a total number of seconddirection sharp pixel-to-pixel transitions for the section (S_(S2));computer readable program code configured to determine whether thesection contains one or more blurred pixel-to-pixel transitions in thesecond direction; computer readable program code configured to count atotal number of second-direction blurred pixel-to-pixel transitions forthe section (S_(B2)); computer readable program code configured todetermine the section is blank upon determining: S_(S1) is less than apredetermined sharp transition threshold, S_(B1) is less than apredetermined blurred transition threshold, S_(S2) is less than apredetermined sharp transition threshold, and S_(B2) is less than apredetermined blurred transition threshold; and computer readableprogram code configured to determine, for all non-blank sections, afirst direction blur ratio r₁=S_(S1)/S_(B1); computer readable programcode configured to determine, for all non-blank sections, a seconddirection blur ratio r₂=S_(S2)/S_(B2); computer readable program codeconfigured to determine that a non-blank section is blurred in the firstdirection upon determine that r₁ is less than a predefined section blurratio threshold; and computer readable program code configured todetermine that a non-blank section is blurred in the second directionupon determine that r₂ is less than the predefined section blur ratiothreshold; and computer readable program code configured to determinethat a non-blank section is blurred upon determine one or more of: thesection is blurred in the first direction; and the section is blurred inthe section direction; and computer readable program code configured todetermine a total number of blurred sections; computer readable programcode configured to calculate an image blur ratio R comprising: the totalnumber blurred sections to a total number of sections; and computerreadable program code configured to determine the digital image isblurred upon determine the image blur ratio is greater than apredetermined image blur threshold.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a network architecture, in accordance with oneembodiment.

FIG. 2 shows a representative hardware environment that may beassociated with the servers and/or clients of FIG. 1, in accordance withone embodiment.

FIG. 3A is a schematic representation of a digital image comprising adigital representation of a document, according to one embodiment.

FIG. 3B is a schematic representation of a digital image comprising adigital representation of a document and a plurality of page detectionanalysis windows, according to one embodiment.

FIG. 3C is a schematic representation of a digital image comprising adigital representation of a document characterized by a plurality ofcandidate edge points, according to one embodiment.

FIG. 3D is a schematic representation of a large analysis windowcomprising a plurality of pixels of a digital image, and a smallanalysis window within the large analysis window, according to oneembodiment.

FIG. 4 is a schematic representation of a digital image comprising adigital representation of a document bounded by a target tetragon,according to one embodiment.

FIG. 5A is a graphical representation of a first iteration of a pagerectangularization algorithm, according to one embodiment.

FIG. 5B is a graphical representation of an input to a pagerectangularization algorithm, according to one embodiment.

FIG. 5C is a graphical representation of an output of a pagerectangularization algorithm, according to one embodiment.

FIG. 6 is a graphical representation of one algorithmic approach todetecting and/or correcting skew of a digital representation of adocument in a digital image, according to one embodiment.

FIG. 7A is a pictorial representation of a digital image comprising adigital representation of a document characterized by unevenillumination, according to one embodiment.

FIG. 7B is a pictorial representation of an output of the digital imageas shown in FIG. 7A after normalizing the uneven illumination, accordingto one embodiment.

FIG. 8A depicts a digital image comprising a digital representation of adocument, according to one embodiment.

FIG. 8B depicts a digital image as shown in FIG. 8A after performing apage detection algorithm on the digital image, the digital image havinga detected digital representation of a document therein, according toone embodiment.

FIG. 8C is depicts a digital representation of a document as shown inFIG. 8B, with the background of the digital image having been removedand a skew angle of the digital representation of the document havingbeen corrected, according to one embodiment.

FIG. 8D is a digital representation of a document as shown in FIG. 8C,with the digital representation of the document having been thresholdedto produce a bitonal image.

FIG. 9 is a flowchart of a method for capturing and/or processing adigital image comprising a digital representation of a document,according to one embodiment.

FIG. 10A is a schematic representation of a user authenticationinterface of an application for capturing and/or processing a digitalimage comprising a digital representation of a document, according toone embodiment.

FIG. 10B is a schematic representation of a host connection userinterface of an application for capturing and/or processing a digitalimage comprising a digital representation of a document, according toone embodiment.

FIG. 11 is a schematic representation of a case creation user interfaceof an application for capturing and/or processing a digital imagecomprising a digital representation of a document, according to oneembodiment.

FIG. 12 is a schematic representation of a case object management userinterface of an application for capturing and/or processing a digitalimage comprising a digital representation of a document, according toone embodiment.

FIG. 13A is a schematic representation of a case object management userinterface of an application for capturing and/or processing a digitalimage comprising a digital representation of a document, according toone embodiment.

FIG. 13B is a schematic representation of a case management action userinterface of an application for capturing and/or processing a digitalimage comprising a digital representation of a document, according toone embodiment.

FIG. 13C is a schematic representation of a delete object user interfaceof an application for capturing and/or processing a digital imagecomprising a digital representation of a document, according to oneembodiment.

FIG. 13D is a schematic representation of an edit object user interfaceof an application for capturing and/or processing a digital imagecomprising a digital representation of a document, according to oneembodiment.

FIG. 13E is a schematic representation of an edit object action userinterface of an application for capturing and/or processing a digitalimage comprising a digital representation of a document, according toone embodiment.

FIG. 13F is a schematic representation of a crop object user interfaceof an application for capturing and/or processing a digital imagecomprising a digital representation of a document, according to oneembodiment.

FIG. 13G is a schematic representation of a constrain object userinterface of an application for capturing and/or processing a digitalimage comprising a digital representation of a document, according toone embodiment.

FIG. 13H is a schematic representation of a case type management userinterface of an application for capturing and/or processing a digitalimage comprising a digital representation of a document, according toone embodiment.

FIG. 13I is a schematic representation of an enter case data userinterface of an application for capturing and/or processing a digitalimage comprising a digital representation of a document, according toone embodiment.

FIG. 13J is a schematic representation of a capture signature userinterface of an application for capturing and/or processing a digitalimage comprising a digital representation of a document, according toone embodiment.

FIG. 13K is a schematic representation of a submit case user interfaceof an application for capturing and/or processing a digital imagecomprising a digital representation of a document, according to oneembodiment.

FIG. 14A is a schematic representation of a print case user interface ofan application for capturing and/or processing a digital imagecomprising a digital representation of a document, according to oneembodiment.

FIG. 14B is a schematic representation of a select printer userinterface of an application for capturing and/or processing a digitalimage comprising a digital representation of a document, according toone embodiment.

FIG. 14C is a schematic representation of a print details user interfaceof an application for capturing and/or processing a digital imagecomprising a digital representation of a document, according to oneembodiment.

FIG. 14D is a schematic representation of a print job user interface ofan application for capturing and/or processing a digital imagecomprising a digital representation of a document, according to oneembodiment.

FIG. 15A is a schematic representation of an image capture userinterface of an application for capturing and/or processing a digitalimage comprising a digital representation of a document, according toone embodiment.

FIG. 15B is a schematic representation of an image capture userinterface of an application for capturing and/or processing a digitalimage comprising a digital representation of a document, according toone embodiment.

FIG. 15C is a schematic representation of an image capture QC resultsuser interface of an application for capturing and/or processing adigital image comprising a digital representation of a document,according to one embodiment.

FIG. 16A is a schematic representation of a capture image attachmentuser interface of an application for capturing and/or processing adigital image comprising a digital representation of a document,according to one embodiment.

FIG. 16B is a schematic representation of a capture audio attachmentuser interface of an application for capturing and/or processing adigital image comprising a digital representation of a document,according to one embodiment.

FIG. 16C is a schematic representation of a capture video attachmentuser interface of an application for capturing and/or processing adigital image comprising a digital representation of a document,according to one embodiment.

FIG. 16D is a schematic representation of a mobile scanner image captureuser interface of an application for capturing and/or processing adigital image comprising a digital representation of a document,according to one embodiment.

FIG. 17 is a schematic representation of a settings user interface of anapplication for capturing and/or processing a digital image comprising adigital representation of a document, according to one embodiment.

FIG. 18 is a schematic representation of a notifications user interfaceof an application for capturing and/or processing a digital imagecomprising a digital representation of a document, according to oneembodiment.

FIG. 19 is a flowchart of a method for page detection, according to oneembodiment.

FIG. 20 is a flowchart of a method for page rectangularization,according to one embodiment.

FIG. 21 is a flowchart of a method for detecting illumination problems,according to one embodiment.

FIG. 22 is a flowchart of a method for correcting illumination problems,according to one embodiment.

FIG. 23 is a flowchart of a method for estimating resolution of adigital image comprising a digital representation of a document,according to one embodiment.

FIG. 24 is a flowchart of a method for detecting blur in a digitalimage, according to one embodiment.

FIG. 25 is a flowchart of a method for providing image processingapplication functionality, according to one embodiment.

FIG. 26 is a flowchart of a method for providing case managementapplication functionality, according to one embodiment.

DETAILED DESCRIPTION

The following description is made for the purpose of illustrating thegeneral principles of the present invention and is not meant to limitthe inventive concepts claimed herein. Further, particular featuresdescribed herein can be used in combination with other describedfeatures in each of the various possible combinations and permutations.

Unless otherwise specifically defined herein, all terms are to be giventheir broadest possible interpretation including meanings implied fromthe specification as well as meanings understood by those skilled in theart and/or as defined in dictionaries, treatises, etc.

It must also be noted that, as used in the specification and theappended claims, the singular forms “a,” “an” and “the” include pluralreferents unless otherwise specified.

The present application refers to image processing of images (e.g.pictures, figures, graphical schematics, single frames of movies,videos, films, clips, etc.) captured by cameras, especially cameras ofmobile devices. As understood herein, a mobile device is any devicecapable of receiving data without having power supplied via a physicalconnection (e.g. wire, cord, cable, etc.) and capable of receiving datawithout a physical data connection (e.g. wire, cord, cable, etc.).Mobile devices within the scope of the present disclosures includeexemplary devices such as a mobile telephone, smartphone, tablet,personal digital assistant, iPod®, iPad®, BLACKBERRY® device, etc.

However, as it will become apparent from the descriptions of variousfunctionalities, the presently disclosed mobile image processingalgorithms can be applied, sometimes with certain modifications, toimages coming from scanners and multifunction peripherals (MFPs).Similarly, images processed using the presently disclosed processingalgorithms may be further processed using conventional scannerprocessing algorithms, in some approaches.

Of course, the various embodiments set forth herein may be implementedutilizing hardware, software, or any desired combination thereof. Forthat matter, any type of logic may be utilized which is capable ofimplementing the various functionality set forth herein.

One benefit of using a mobile device is that with a data plan, imageprocessing and information processing based on captured images can bedone in a much more convenient, streamlined and integrated way thanprevious methods that relied on presence of a scanner. However, the useof mobile devices as document(s) capture and/or processing devices hasheretofore been considered unfeasible for a variety of reasons.

In one approach, an image may be captured by a camera of a mobiledevice. The term “camera” should be broadly interpreted to include anytype of device capable of capturing an image of a physical objectexternal to the device, such as a piece of paper. The term “camera” doesnot encompass a peripheral scanner or multifunction device. Any type ofcamera may be used. Preferred embodiments may use cameras having ahigher resolution, e.g. 8 MP or more, ideally 12 MP or more. The imagemay be captured in color, grayscale, black and white, or with any otherknown optical effect. The term “image” as referred to herein is meant toencompass any type of data corresponding to the output of the camera,including raw data, processed data, etc.

General Embodiments

In one general embodiment, a method includes dividing, using aprocessor, a tetragon comprising a digital representation of a documentin a digital image into a plurality of sections, each section comprisinga plurality of pixels; for each section: determining whether the sectioncontains one or more sharp pixel-to-pixel transitions in a firstdirection; counting a total number of first direction sharppixel-to-pixel transitions for the section (S_(S1)); determining whetherthe section contains one or more blurred pixel-to-pixel transitions inthe first direction; counting a total number of first-direction blurredpixel-to-pixel transitions for the section (S_(B1)); determining whetherthe section contains one or more sharp pixel-to-pixel transitions in asecond direction; counting a total number of second-direction sharppixel-to-pixel transitions for the section (S_(S2)); determining whetherthe section contains one or more blurred pixel-to-pixel transitions inthe second direction; counting a total number of second-directionblurred pixel-to-pixel transitions for the section (S_(B2)); determiningthe section is blank upon determining: S_(S1) is less than apredetermined sharp transition threshold, S_(B1) is less than apredetermined blurred transition threshold, S_(S2) is less than apredetermined sharp transition threshold, and S_(B2) is less than apredetermined blurred transition threshold; and determining, for allnon-blank sections, a first direction blur ratio r₁=S_(S1)/S_(B1);determining, for all non-blank sections, a second direction blur ratior₂=S_(S2)/S_(B2); determining that a non-blank section is blurred in thefirst direction upon determining that r₁ is less than a predefinedsection blur ratio threshold; and determining that a non-blank sectionis blurred in the second direction upon determining that r₂ is less thanthe predefined section blur ratio threshold; and determining that anon-blank section is blurred upon determining one or more of: thesection is blurred in the first direction, and the section is blurred inthe section direction; and determining a total number of blurredsections; calculating an image blur ratio R comprising: the total numberblurred sections to a total number of sections; and determining thedigital image is blurred upon determining the image blur ratio isgreater than a predetermined image blur threshold.

In another general embodiment, a system includes a processor configuredto execute logic; logic for dividing, using a processor, a tetragoncomprising a digital representation of a document in a digital imageinto a plurality of sections, each section comprising a plurality ofpixels; logic for determining whether the section contains one or moresharp pixel-to-pixel transitions in a first direction; logic forcounting a total number of first direction sharp pixel-to-pixeltransitions for the section (S_(S1)); logic for determining whether thesection contains one or more blurred pixel-to-pixel transitions in thefirst direction; logic for counting a total number of first-directionblurred pixel-to-pixel transitions for the section (S_(B1)); logic fordetermining whether the section contains one or more sharppixel-to-pixel transitions in a second direction; logic for counting atotal number of second direction sharp pixel-to-pixel transitions forthe section (S_(S2)); logic for determining whether the section containsone or more blurred pixel-to-pixel transitions in the second direction;logic for counting a total number of second-direction blurredpixel-to-pixel transitions for the section (S_(B2)); logic fordetermining the section is blank upon determining: S_(S1) is less than apredetermined sharp transition threshold, S_(B1) is less than apredetermined blurred transition threshold, S_(S2) is less than apredetermined sharp transition threshold, and S_(B2) is less than apredetermined blurred transition threshold; and logic for determining,for all non-blank sections, a first direction blur ratior₁=S_(S1)/S_(B1); logic for determining, for all non-blank sections, asecond direction blur ratio r₂=S_(S2)/S_(B2); logic for determining thata non-blank section is blurred in the first direction upon determiningthat r₁ is less than a predefined section blur ratio threshold; andlogic for determining that a non-blank section is blurred in the seconddirection upon determining that r₂ is less than the predefined sectionblur ratio threshold; and logic for determining that a non-blank sectionis blurred upon determining one or more of: the section is blurred inthe first direction; and the section is blurred in the sectiondirection; and logic for determining a total number of blurred sections;logic for calculating an image blur ratio R comprising: the total numberblurred sections to a total number of sections; and logic fordetermining the digital image is blurred upon determining the image blurratio is greater than a predetermined image blur threshold.

In yet another general embodiment, a computer program product includes acomputer readable storage medium having computer readable program codestored thereon, the computer readable program code including computerreadable program code configured to divide, use a processor, a tetragoncomprise a digital representation of a document in a digital image intoa plurality of sections, each section comprise a plurality of pixels;computer readable program code configured to determine whether thesection contains one or more sharp pixel-to-pixel transitions in a firstdirection; computer readable program code configured to count a totalnumber of first direction sharp pixel-to-pixel transitions for thesection (S_(S1)); computer readable program code configured to determinewhether the section contains one or more blurred pixel-to-pixeltransitions in the first direction; computer readable program codeconfigured to count a total number of first-direction blurredpixel-to-pixel transitions for the section (S_(B1)); computer readableprogram code configured to determine whether the section contains one ormore sharp pixel-to-pixel transitions in a second direction; computerreadable program code configured to count a total number of seconddirection sharp pixel-to-pixel transitions for the section (S_(S2));computer readable program code configured to determine whether thesection contains one or more blurred pixel-to-pixel transitions in thesecond direction; computer readable program code configured to count atotal number of second-direction blurred pixel-to-pixel transitions forthe section (S_(B2)); computer readable program code configured todetermine the section is blank upon determining: S_(S1) is less than apredetermined sharp transition threshold, S_(B1) is less than apredetermined blurred transition threshold, S_(S2) is less than apredetermined sharp transition threshold, and S_(B2) is less than apredetermined blurred transition threshold; and computer readableprogram code configured to determine, for all non-blank sections, afirst direction blur ratio r₁=S_(S1)/S_(B1); computer readable programcode configured to determine, for all non-blank sections, a seconddirection blur ratio r₂=S_(S2)/S_(B2); computer readable program codeconfigured to determine that a non-blank section is blurred in the firstdirection upon determine that r₁ is less than a predefined section blurratio threshold; and computer readable program code configured todetermine that a non-blank section is blurred in the second directionupon determine that r₂ is less than the predefined section blur ratiothreshold; and computer readable program code configured to determinethat a non-blank section is blurred upon determine one or more of: thesection is blurred in the first direction; and the section is blurred inthe section direction; and computer readable program code configured todetermine a total number of blurred sections; computer readable programcode configured to calculate an image blur ratio R comprising: the totalnumber blurred sections to a total number of sections; and computerreadable program code configured to determine the digital image isblurred upon determine the image blur ratio is greater than apredetermined image blur threshold.

An application may be installed on the mobile device, e.g., stored in anonvolatile memory of the device. In one approach, the applicationincludes instructions to perform processing of an image on the mobiledevice. In another approach, the application includes instructions tosend the image to a remote server such as a network server. In yetanother approach, the application may include instructions to decidewhether to perform some or all processing on the mobile device and/orsend the image to the remote site. Examples of how an image may beprocessed are presented in more detail below.

One illustrative methodology for correction of projective and non-linearoptical effects is an extension of a known algorithm for edge detection,such as the algorithm(s) described in U.S. Pat. Nos. 7,545,529 and6,370,277, which are herein incorporated by reference. Such illustrativemethodologies may include some or all of the algorithmic featuresdisclosed herein as the extension on known algorithms, which do notinclude the specific functionalities disclosed herein.

It may be useful to understand how page detection is performed prior todiscussing the differences introduced in order to deal with imagescaptured by area sensors (cameras). In one approach, the edge detectionalgorithm goes from the boundaries of the image into the image, lookingfor points that are sufficiently different from what is known about theproperties of the background. However, the background in the imagescaptured by even the same mobile device may be different every time, soa new technique to identify the document(s) in the image is provided.

In one embodiment, edges of the document(s) are detected. Any method ofedge detection known in the art may be used. For example, the techniquedescribed in U.S. patent application Ser. No. 12/206,594, filed Sep. 8,2008 and which is incorporated by reference, may be used. Moreover, anoutside-to-inside edge detection technique, inside-to-outside edgedetection technique, or combination of both may be used.

Turning now to the figures, FIG. 1 illustrates a network architecture100, in accordance with one embodiment. As shown in FIG. 1, a pluralityof remote networks 102 are provided including a first remote network 104and a second remote network 106. A gateway 101 may be coupled betweenthe remote networks 102 and a proximate network 108. In the context ofthe present network architecture 100, the networks 104, 106 may eachtake any form including, but not limited to a LAN, a WAN such as theInternet, public switched telephone network (PSTN), internal telephonenetwork, etc.

In use, the gateway 101 serves as an entrance point from the remotenetworks 102 to the proximate network 108. As such, the gateway 101 mayfunction as a router, which is capable of directing a given packet ofdata that arrives at the gateway 101, and a switch, which furnishes theactual path in and out of the gateway 101 for a given packet.

Further included is at least one data server 114 coupled to theproximate network 108, and which is accessible from the remote networks102 via the gateway 101. It should be noted that the data server(s) 114may include any type of computing device/groupware. Coupled to each dataserver 114 is a plurality of user devices 116. Such user devices 116 mayinclude a desktop computer, la p-top computer, hand-held computer,mobile device, printer or any other type of logic. It should be notedthat a user device 111 may also be directly coupled to any of thenetworks, in one embodiment.

A peripheral 120 or series of peripherals 120, e.g., facsimile machines,printers, networked and/or local storage units or systems, etc., may becoupled to one or more of the networks 104, 106, 108. It should be notedthat databases and/or additional components may be utilized with, orintegrated into, any type of network element coupled to the networks104, 106, 108. In the context of the present description, a networkelement may refer to any component of a network.

According to some approaches, methods and systems described herein maybe implemented with and/or on virtual systems and/or systems whichemulate one or more other systems, such as a UNIX system which emulatesan IBM z/OS environment, a UNIX system which virtually hosts a MICROSOFTWINDOWS environment, a MICROSOFT WINDOWS system which emulates an IBMz/OS environment, etc. This virtualization and/or emulation may beenhanced through the use of VMWARE software, in some embodiments.

In more approaches, one or more networks 104, 106, 108, may represent acluster of systems commonly referred to as a “cloud.” In cloudcomputing, shared resources, such as processing power, peripherals,software, data, servers, etc., are provided to any system in the cloudin an on-demand relationship, thereby allowing access and distributionof services across many computing systems. Cloud computing typicallyinvolves an Internet connection between the systems operating in thecloud, but other techniques of connecting the systems may also be used.

FIG. 2 shows a representative hardware environment associated with auser device 116 and/or server 114 of FIG. 1, in accordance with oneembodiment. FIG. 2 illustrates a typical hardware configuration of aworkstation having a central processing unit (CPU) 210, such as amicroprocessor, and a number of other units interconnected via one ormore buses 212 which may be of different types, such as a local bus, aparallel bus, a serial bus, etc., according to several embodiments.

The workstation shown in FIG. 2 includes a Random Access Memory (RAM)214, Read Only Memory (ROM) 216, an I/O adapter 218 for connectingperipheral devices such as disk storage units 220 to the one or morebuses 212, a user interface adapter 222 for connecting a keyboard 224, amouse 226, a speaker 228, a microphone 232, and/or other user interfacedevices such as a touch screen, a digital camera (not shown), etc., tothe one or more buses 212, communication adapter 234 for connecting theworkstation to a communication network 235 (e.g., a data processingnetwork) and a display adapter 236 for connecting the one or more buses212 to a display device 238.

The workstation may have resident thereon an operating system such asthe MICROSOFT WINDOWS Operating System (OS), a MAC OS, a UNIX OS, etc.It will be appreciated that a preferred embodiment may also beimplemented on platforms and operating systems other than thosementioned. A preferred embodiment may be written using JAVA, XML, C,and/or C++ language, or other programming languages, along with anobject oriented programming methodology. Object oriented programming(OOP), which has become increasingly used to develop complexapplications, may be used.

The description herein is presented to enable any person skilled in theart to make and use the invention and is provided in the context ofparticular applications of the invention and their requirements. Variousmodifications to the disclosed embodiments will be readily apparent tothose skilled in the art and the general principles defined herein maybe applied to other embodiments and applications without departing fromthe spirit and scope of the present invention. Thus, the presentinvention is not intended to be limited to the embodiments shown, but isto be accorded the widest scope consistent with the principles andfeatures disclosed herein.

In particular, various embodiments of the invention discussed herein areimplemented using the Internet as a means of communicating among aplurality of computer systems. One skilled in the art will recognizethat the present invention is not limited to the use of the Internet asa communication medium and that alternative methods of the invention mayaccommodate the use of a private intranet, a Local Area Network (LAN), aWide Area Network (WAN) or other means of communication. In addition,various combinations of wired, wireless (e.g., radio frequency) andoptical communication links may be utilized.

The program environment in which one embodiment of the invention may beexecuted illustratively incorporates one or more general-purposecomputers or special-purpose devices such hand-held computers. Detailsof such devices (e.g., processor, memory, data storage, input and outputdevices) are well known and are omitted for the sake of brevity.

It should also be understood that the techniques of the presentinvention might be implemented using a variety of technologies. Forexample, the methods described herein may be implemented in softwarerunning on a computer system, or implemented in hardware utilizing oneor more processors and logic (hardware and/or software) for performingoperations of the method, application specific integrated circuits,programmable logic devices such as Field Programmable Gate Arrays(FPGAs), and/or various combinations thereof. In one illustrativeapproach, methods described herein may be implemented by a series ofcomputer-executable instructions residing on a storage medium such as aphysical (e.g., non-transitory) computer-readable medium. In addition,although specific embodiments of the invention may employobject-oriented software programming concepts, the invention is not solimited and is easily adapted to employ other forms of directing theoperation of a computer.

The invention can also be provided in the form of a computer programproduct comprising a computer readable storage or signal medium havingcomputer code thereon, which may be executed by a computing device(e.g., a processor) and/or system. A computer readable storage mediumcan include any medium capable of storing computer code thereon for useby a computing device or system, including optical media such as readonly and writeable CD and DVD, magnetic memory or medium (e.g., harddisk drive, tape), semiconductor memory (e.g., FLASH memory and otherportable memory cards, etc.), firmware encoded in a chip, etc.

A computer readable signal medium is one that does not fit within theaforementioned storage medium class. For example, illustrative computerreadable signal media communicate or otherwise transfer transitorysignals within a system, between systems e.g., via a physical or virtualnetwork, etc.

It will be clear that the various features of the foregoingmethodologies may be combined in any way, creating a plurality ofcombinations from the descriptions presented above.

It will also be clear to one skilled in the art that the methodology ofthe present invention may suitably be embodied in a logic apparatuscomprising logic to perform various steps of the methodology presentedherein, and that such logic may comprise hardware components or firmwarecomponents.

It will be equally clear to one skilled in the art that the logicarrangement in various approaches may suitably be embodied in a logicapparatus comprising logic to perform various steps of the method, andthat such logic may comprise components such as logic gates in, forexample, a programmable logic array. Such a logic arrangement mayfurther be embodied in enabling means or components for temporarily orpermanently establishing logical structures in such an array using, forexample, a virtual hardware descriptor language, which may be storedusing fixed or transmittable carrier media.

It will be appreciated that the methodology described above may alsosuitably be carried out fully or partially in software running on one ormore processors (not shown), and that the software may be provided as acomputer program element carried on any suitable data carrier (also notshown) such as a magnetic or optical computer disc. The channels for thetransmission of data likewise may include storage media of alldescriptions as well as signal carrying media, such as wired or wirelesssignal media.

Embodiments of the present invention may suitably be embodied as acomputer program product for use with a computer system. Such animplementation may comprise a series of computer readable instructionseither fixed on a tangible medium, such as a computer readable medium,for example, diskette, CD-ROM, ROM, or hard disk, or transmittable to acomputer system, via a modem or other interface device, over either atangible medium, including but not limited to optical or analoguecommunications lines, or intangibly using wireless techniques, includingbut not limited to microwave, infrared or other transmission techniques.The series of computer readable instructions embodies all or part of thefunctionality previously described herein.

Those skilled in the art will appreciate that such computer readableinstructions can be written in a number of programming languages for usewith many computer architectures or operating systems. Further, suchinstructions may be stored using any memory technology, present orfuture, including but not limited to, semiconductor, magnetic, oroptical, or transmitted using any communications technology, present orfuture, including but not limited to optical, infrared, or microwave. Itis contemplated that such a computer program product may be distributedas a removable medium with accompanying printed or electronicdocumentation, for example, shrink-wrapped software, pre-loaded with acomputer system, for example, on a system ROM or fixed disk, ordistributed from a server or electronic bulletin board over a network,for example, the Internet or World Wide Web.

Communications components such as input/output or I/O devices (includingbut not limited to keyboards, displays, pointing devices, etc.) can becoupled to the system either directly or through intervening I/Ocontrollers.

Communications components such as buses, interfaces, network adapters,etc. may also be coupled to the system to enable the data processingsystem, e.g., host, to become coupled to other data processing systemsor remote printers or storage devices through intervening private orpublic networks. Modems, cable modem and Ethernet cards are just a fewof the currently available types of network adapters.

Various Embodiments of a Mobile Image Capture and Processing Algorithm

Various embodiments of a Mobile Image Capture and Processing algorithm,as well as several mobile applications configured to facilitate use ofsuch algorithmic processing within the scope of the present disclosuresare described below. It is to be appreciated that each section belowdescribes functionalities that may be employed in any combination withthose disclosed in other sections, including any or up to all thefunctionalities described herein. Moreover, functionalities of theprocessing algorithm embodiments as well as the mobile applicationembodiments may be combined and/or distributed in any manner across avariety of computing resources and/or systems, in several approaches.

An application may be installed on the mobile device, e.g., stored in anonvolatile memory of the device. In one approach, the applicationincludes instructions to perform processing of an image on the mobiledevice. In another approach, the application includes instructions tosend the image to one or more non-mobile devices, e.g. a remote serversuch as a network server, a remote workstation, a cloud computingenvironment, etc. as would be understood by one having ordinary skill inthe art upon reading the present descriptions. In yet another approach,the application may include instructions to decide whether to performsome or all processing on the mobile device and/or send the image to theremote site. Examples of how an image may be processed are presented inmore detail below.

In one embodiment, there may be no difference between the processingthat may be performed on the mobile device and a remote server, otherthan speed of processing, constraints on memory available, etc.Moreover, there may be some or no difference between various userinterfaces presented on a mobile device, e.g. as part of a mobileapplication, and corresponding user interfaces presented on a display incommunication with the non-mobile device.

In other embodiments, a remote server may have higher processing power,more capabilities, more processing algorithms, etc. In yet furtherembodiments, the mobile device may have no image processing capabilityassociated with the application, other than that required to send theimage to the remote server. In yet another embodiment, the remote servermay have no image processing capability relevant to the platformspresented herein, other than that required to receive the processedimage from the remote server. Accordingly, the image may be processedpartially or entirely on the mobile device, and/or partially or entirelyon a remote server, and/or partially or entirely in a cloud, and/orpartially or entirely in any part of the overall architecture inbetween. Moreover, some processing steps may be duplicated on differentdevices.

Which device performs which parts of the processing may be defined by auser, may be predetermined, may be determined on the fly, etc. Moreover,some processing steps may be re-performed, e.g., upon receiving arequest from the user. Accordingly, the raw image data, partiallyprocessed image data, or fully processed image data may be transmittedfrom the mobile device, e.g., using a wireless data network, to a remotesystem. Image data as processed at a remote system may be returned tothe mobile device for output and/or further processing.

In a further approach, the image may be partitioned, and the processingof the various parts may be allocated to various devices, e.g., ½ to themobile device and ½ to the remote server, after which the processedhalves are combined.

In one embodiment, selection of which device performs the processing maybe based at least in part on a relative speed of processing locally onthe mobile device vs. communication with the server.

In one approach, a library of processing functions may be present, andthe application on the mobile device or the application on a remoteserver simply makes calls to this library, and essentially the meaningof the calls defines what kind of processing to perform. The device thenperforms that processing and outputs the processed image, perhaps withsome corresponding metadata.

Any type of image processing known in the art and/or as newly presentedherein may be performed in any combination in various embodiments.

Referring now to illustrative image processing, the camera can beconsidered an area sensor that captures images, where the images mayhave any number of projective effects, and sometimes non-linear effects.The image may be processed to correct for such effects. Moreover, theposition and boundaries of the document(s) in the image may be foundduring the processing, e.g., the boundaries of one or more actual pagesof paper in the background surrounding the page(s). Because of themobile nature of various embodiments, the sheet of paper may be lying onjust about anything. This complicates image analysis in comparison toprocessing images of documents produced using a scanner, because scannerbackground properties are constant and typically known, whereas mobilecapture backgrounds may vary almost infinitely according to the locationof the document and the corresponding surrounding textures captured inthe image background, as well as because of variable lightingconditions.

Accordingly, the non-uniformity of the background of the surface onwhich the piece of paper may be positioned for capture by the camerapresents one challenge, and the non-linear and projective effectspresent additional challenges. Various embodiments overcome thesechallenges, as will soon become apparent.

In one exemplary mode of operation, an application on the mobile devicemay be initiated, e.g., in response to a user request to open theapplication. For example, a user-selection of an icon representing theapplication may be detected.

In some approaches, a user authentication may be requested and/orperformed. For example, a user ID and password, or any otherauthentication information, may be requested and/or received from theuser.

In further approaches, various tasks may be enabled via a graphical userinterface of the application. For example, a list of tasks may bepresented. In such case, a selection of one of the tasks by the user maybe detected, and additional options may be presented to the user, apredefined task may be initiated, the camera may be initiated, etc.

An image may be captured by the camera of the mobile device, preferablyupon receiving some type of user input such as detecting a tap on ascreen of the mobile device, depression of a button on the mobiledevice, a voice command, a gesture, etc. Another possible scenario mayinvolve some level of analysis of sequential frames, e.g. from a videostream. Sequential frame analysis may be followed by a switch tocapturing a single high-resolution image frame, which may be triggeredautomatically or by a user, in some approaches. Moreover, the triggermay be based on information received from one or more mobile devicesensors. For example, in one embodiment an accelerometer in or coupledto the mobile device may indicate a stability of the camera, and theapplication may analyze low-resolution video frame(s) for a document. Ifa document is detected, the application may perform a focusing operationand acquire a high-resolution image of the detected document. Either thelow- or high-resolution image may be further processed, but preferredembodiments utilize the high-resolution image for subsequent processing.In more approaches, switching to single frame mode as discussed abovemay be unnecessary, particularly for smaller documents such as businesscards and receipts. To increase processing rate and reduce consumptionof processing resources, document type identification may facilitatedetermining whether or not to switch to single frame mode and/or capturea high-resolution image for processing. For the present discussion,assume an image of one or more documents is captured.

Given that mobile devices do not typically have the processing power ofconventional non-mobile devices, one approach performs some limitedprocessing on the mobile device, for example to let the user verify thatthe page(s) has been found correctly, that the image is not blurred,and/or that the lighting is adequate, e.g., a preview of sorts.

In one approach, the document(s) within the image captured by the cameramay be found.

Additional methods of detecting one or more boundaries of thedocument(s) are also presented herein. If the document(s) in the imagehas nonlinearities or is not rectangular, correction processing may beapplied.

Once the page(s) are found in the image, one embodiment performs asmooth transformation in order to make the page(s) rectangular, assumingof course the original piece of paper was rectangular. Another usefulcorrection to the image may be mitigation of the unevenness of theillumination.

In one exemplary approach, page detection and rectangularization may beperformed substantially as described below.

Various Embodiments of Mobile Page Detection

One exemplary embodiment illustrating an exemplary methodology forperforming page detection will now be described with reference to FIGS.3A-4. With reference to these descriptions, it will become more clearhow the advantages implemented for a mobile processing algorithm asdescribed herein handle images captured by area sensors (cameras) andcompensate for the inherent difficulties presented thereby.

In one approach, and with particular reference to FIGS. 3A-3B, an edgedetection algorithm proceeds from the boundaries of a digital image 300toward a central region of the image 300, looking for points that aresufficiently different from what is known about the properties of thebackground.

Notably, the background 304 in the images captured by even the samemobile device may be different every time, so a new technique toidentify the document(s) in the image is provided.

Finding page edges within a camera-captured image according to thepresent disclosures helps to accommodate important differences in theproperties of images captured using mobile devices as opposed, e.g., toscanners. For example, due to projective effects the image of arectangular document in a photograph may not appear truly rectangular,and opposite sides of the document in the image may not have the samelength. Second, even the best lenses have some non-linearity resultingin straight lines within an object, e.g. straight sides of asubstantially rectangular document, appearing slightly curved in thecaptured image of that object. Third, images captured using camerasoverwhelmingly tend to introduce uneven illumination effects in thecaptured image. This unevenness of illumination makes even a perfectlyuniform background of the surface against which a document may be placedappear in the image with varied brightness, and often with shadows,especially around the page edges if the page is not perfectly flat.

In an exemplary approach, to avoid mistaking the variability within thebackground for page edges, the current algorithm utilizes one or more ofthe following functionalities.

In various embodiments, the frame of the image contains the digitalrepresentation of the document 302 with margins of the surroundingbackground 304. In the preferred implementation the search forindividual page edges 306 may be performed on a step-over approachanalyzing rows and columns of the image from outside in. In oneembodiment, the step-over approach may define a plurality of analysiswindows 308 within the digital image 300, such as shown in FIGS. 3A-3B.As understood herein, analysis windows 308 may include one or more“background windows,” i.e. windows encompassing only pixels depictingthe background 304 of the digital image 300, as well as one or more“test windows” i.e. windows encompassing pixels depicting the background304 of the digital image 300, the digital representation of the document302, or both.

In a preferred embodiment, the digital representation of the documentmay be detected in the digital image by defining a first analysis window308, i.e. a background analysis window, in a margin of the imagecorresponding to the background 304 of the surface upon which thedocument is placed. Within the first analysis window 308, a plurality ofsmall analysis windows (e.g. test windows 312 as shown in FIG. 3D) maybe defined within the first analysis window 308. Utilizing the pluralityof test windows 312, one or more distributions of one or morestatistical properties descriptive of the background 304 may beestimated.

With continuing reference to the preferred embodiment discussedimmediately above, a next step in detecting boundaries of the digitalrepresentation of the document may include defining a plurality of testwindows 312 within the digital image, and analyzing the correspondingregions of the digital image. For each test window 312 one or morestatistical values descriptive of the corresponding region of the imagemay be calculated. Further, these statistical values may be compared toa corresponding distribution of statistics descriptive of the background304.

In a preferred approach, the plurality of test windows 312 may bedefined along a path, particularly a linear path. In a particularlypreferred approach, the plurality of test windows 312 may be defined ina horizontal direction and/or a vertical direction, e.g. along rows andcolumns of the digital image. Moreover, a stepwise progression may beemployed to define the test windows 312 along the path and/or betweenthe rows and/or columns. In some embodiments, as will be appreciated byone having ordinary skill in the art upon reading the presentdescriptions, utilizing a stepwise progression may advantageouslyincrease the computational efficiency of document detection processes.

Moreover, the magnitude of the starting step may be estimated based onthe resolution or pixel size of the image, in some embodiments, but thisstep may be reduced if advantageous for reliable detection of documentsides, as discussed further below.

In more embodiments, the algorithm estimates the distribution of severalstatistics descriptive of the image properties found in a large analysiswindow 308 placed within the background surrounding the document. In oneapproach a plurality of small windows 312 may be defined within thelarge analysis window 308, and distributions of statistics descriptiveof the small test windows 312 may be estimated. In one embodiment, largeanalysis window 308 is defined in a background region of the digitalimage, such as a top-left corner of the image.

Statistics descriptive of the background pixels may include anystatistical value that may be generated from digital image data, such asa minimum value, a maximum value, a median value, a mean value, a spreador range of values, a variance, a standard deviation, etc. as would beunderstood by one having ordinary skill in the art upon reading thepresent descriptions. Values may be sampled from any data descriptive ofthe digital image 300, such as brightness values in one or more colorchannels, e.g. red-green-blue or RGB, cyan-magenta, yellow, black orCMYK, hue saturation or HSV, etc. as would be understood by one havingordinary skill in the art upon reading the present descriptions.

As shown in FIG. 3D, each of the small analysis windows 312 may comprisea subset of the plurality of pixels within the large analysis window308. Moreover, small analysis windows 312 may be of any size and/orshape capable of fitting within the boundaries of large analysis window308. In a preferred embodiment, small analysis windows 312 may becharacterized by a rectangular shape, and even more preferably arectangle characterized by being three pixels long in a first direction(e.g. height) and seven pixels long in a second direction (e.g. width).Of course, other small analysis window sizes, shapes, and dimensions arealso suitable for implementation in the presently disclosed processingalgorithms.

In one embodiment, test windows may be employed to analyze an image anddetect the boundary of a digital representation of a document depictedin the image. Background windows are used for estimation of originalstatistical properties of the background and/or reestimation of localstatistical properties of the background. Reestimation may be necessaryand/or advantageous in order to address artifacts such as unevenillumination and/or background texture variations.

Preferably, statistical estimation may be performed over some or all ofa plurality of small analysis window(s) 312 in a large analysis window308 within the margin outside of the document page in some approaches.Such estimation may be performed using a stepwise movement of a smallanalysis window 312 within the large analysis window 308, and thestepwise movement may be made in any suitable increment so as to varythe number of samples taken for a given pixel. For example, to promotecomputational efficiency, an analysis process may define a number ofsmall analysis windows 312 within large analysis window 308 sufficientto ensure each pixel 318 is sampled once. Thus the plurality of smallanalysis windows 312 defined in this computationally efficient approachwould share common borders but not overlap.

In another approach designed to promote robustness of statisticalestimations, the analysis process may define a number of small analysiswindows 312 within large analysis window 308 sufficient to ensure eachpixel 318 is sampled a maximum number of times, e.g. by reducing thestep to produce only a single pixel shift in a given direction betweensequentially defined small analysis windows 312. Of course, any stepincrement may be employed in various embodiments of the presentlydisclosed processing algorithms, as would be understood by one havingordinary skill in the art upon reading the present descriptions.

The skilled artisan will appreciate that large analysis windows 308utilized to reestimate statistics of local background in the digitalimage as well as test windows can be placed in the digital image in anywhich way desirable.

For example, according to one embodiment shown in FIG. 3A, the searchfor the left side edge in a given row i begins from the calculation ofthe above mentioned statistics in a large analysis window 308 adjacentto the frame boundary on the left side of the image centered around agiven row i.

In still more embodiments, when encountering a possible non-backgroundtest window (e.g. a test window for which the estimated statistics aredissimilar from the distribution of statistics characteristic of thelast known local background) as the algorithm progresses from the outerregion(s) of the image towards the interior regions thereof, thealgorithm may backtrack into a previously determined background region,form a new large analysis window 308 and reestimate the distribution ofbackground statistics in order to reevaluate the validity of thedifferences between the chosen statistics within the small analysiswindow 312 and the local distribution of corresponding statistics withinthe large analysis window 308, in some embodiments.

As will be appreciated by one having ordinary skill in the art uponreading the present descriptions, the algorithm may proceed from anouter region of the image 300 to an inner region of the image 300 in avariety of manners. For example, in one approach the algorithm proceedsdefining test windows 312 in a substantially spiral pattern. In otherapproaches the pattern may be substantially serpentine along either avertical or a horizontal direction. In still more approaches the patternmay be a substantially shingled pattern. The pattern may also be definedby a “sequence mask” laid over part or all of the digital image 300,such as a checkerboard pattern, a vertically, horizontally, ordiagonally striped pattern, concentric shapes, etc. as would beunderstood by one having ordinary skill in the art upon reading thepresent descriptions. In other embodiments, analysis windows such aslarge analysis windows 308 and/or small analysis windows 312 may bedefined throughout the digital image 300 in a random manner, apseudo-random manner, stochastically, etc. according to some definedprocedure, as would be understood by one having ordinary skill in theart upon reading the present descriptions. The algorithm can proceedwith a sequence of test windows in any desirable fashion as long as thepath allows to backtrack into known background, and the path covers thewhole image with desirable granularity.

Advantageously, recalculating statistics in this manner helps toaccommodate for any illumination drift inherent to the digital image 300and/or background 304, which may otherwise result in falseidentification of non-background points in the image (e.g. outliercandidate edge points 316 as shown in FIG. 3C.)

In still yet more embodiments, when the difference is statisticallyvalid, the algorithm may jump a certain distance further along its pathin order to check again and thus bypass small variations in the textureof the background 304, such as wood grain, scratches on a surface,patterns of a surface, small shadows, etc. as would be understood by onehaving ordinary skill in the art upon reading the present descriptions.

In additional and/or alternative embodiments, after a potentialnon-background point has been found, the algorithm determines whetherthe point lies on the edge of the shadow (a possibility especially ifthe edge of the page is raised above the background surface) and triesto get to the actual page edge. This process relies on the observationthat shadows usually darken towards the real edge followed by an abruptbrightening of the image.

The above described approach to page edge detection was utilized becausethe use of standard edge detectors may be unnecessary and evenundesirable, for several reasons. First, most standard edge detectorsinvolve operations that are time consuming, and second, the instantalgorithm is not concerned with additional requirements like monitoringhow thin the edges are, which directions they follow, etc. Even moreimportantly, looking for page edges 306 does not necessarily involveedge detection per se, i.e. page edge detection according to the presentdisclosures may be performed in a manner that does not search for adocument boundary (e.g. page edge 306), but rather searches for imagecharacteristics associated with a transition from background to thedocument. For example, the transition may be characterized by flatteningof the off-white brightness levels within a glossy paper, i.e. bychanges in texture rather than in average gray or color levels.

As a result, it is possible to obtain candidate edge points (e.g.candidate edge points 314 as shown in FIG. 3C) that are essentially thefirst and the last non-background pixels in each row and column on agrid. In order to eliminate random outliers (e.g. outlier candidate edgepoints 316 as shown in FIG. 3C) and to determine which candidate edgepoints 314 correspond to each side of the page, it is useful in oneapproach to analyze neighboring candidate edge points.

In one embodiment, a “point” may be considered any region within thedigital image, such as a pixel, a position between pixels (e.g. a pointwith fractional coordinates such as the center of a 2-pixel by 2-pixelsquare) a small window of pixels, etc. as would be understood by onehaving ordinary skill in the art upon reading the present descriptions.In a preferred embodiment, a candidate edge point is associated with thecenter of a test window (e.g. a 3-pixel×7-pixel window) that has beenfound to be characterized by statistics that are determined to bedifferent from the distribution of statistics descriptive of the localbackground.

As understood herein, a “neighboring” candidate edge point, or a“neighboring” pixel is considered to be a point or pixel, respectively,which is near or adjacent a point or pixel of interest (e.g. pixel 318),e.g. a point or pixel positioned at least in part along a boundary ofthe point or pixel of interest, a point or pixel positioned within athreshold distance of the point or pixel of interest (such as within 2,10, 64 pixels, etc. in a given direction, within one row of the point orpixel of interest, within one column of the point or pixel of interest),etc. as would be understood by one having ordinary skill in the art uponreading the present descriptions. In preferred approaches, the“neighboring” point or pixel may be the closest candidate edge point tothe point of interest along a particular direction, e.g a horizontaldirection and/or a vertical direction.

Each “good” edge point ideally has at least two immediate neighbors (oneon each side) and does not deviate far from a straight line segmentconnecting these neighbors and the “good” edge point, e.g. the candidateedge point and the at least two immediately neighboring points may befit to a linear regression, and the result may be characterized by acoefficient of determination (R²) not less than 0.95. The angle of thissegment with respect to one or more borders of the digital image,together with its relative location determines whether the edge point isassigned to top, left, right, or bottom side of the page. In a preferredembodiment, a candidate edge point and the two neighboring edge pointsmay be assigned to respective corners of a triangle. If the angle of thetriangle at the candidate edge point is close to 180 degrees, then thecandidate edge point may be considered a “good” candidate edge point. Ifthe angle of the triangle at the candidate edge point deviates far from180 degrees by more than a threshold value (such as by 20 degrees ormore), then the candidate edge point may be excluded from the set of“good” candidate edge points. The rationale behind this heuristic isbased on the desire to throw out random errors in the determination ofthe first and last non-background pixels within rows and columns. Thesepixels are unlikely to exist in consistent lines, so checking theneighbors in terms of distance and direction is particularlyadvantageous in some approaches.

For speed, the step of this grid may start from a large number such as32, but it may be reduced by a factor of two and the search for edgepoints repeated until there are enough of them to determine the LeastMean Squares (LMS) based equations of page sides (see below). If thisprocess cannot determine the sides reliably even after using all rowsand columns in the image, it gives up and the whole image is treated asthe page.

The equations of page sides are determined as follows, in oneembodiment. First, the algorithm fits the best LMS straight line to eachof the sides using the strategy of throwing out worst outliers until allthe remaining supporting edges lie within a small distance from the LMSline. For example, a point with the largest distance from asubstantially straight line connecting a plurality of candidate edgepoints along a particular boundary of the document may be designated the“worst” outlier. This procedure may be repeated iteratively to designateand/or remove one or more “worst” outliers from the plurality ofcandidate edge point. In some approaches, the distance with which acandidate edge point may deviate from the line connecting the pluralityof candidate edge points is based at least in part on the size and/orresolution of the digital image.

If this line is not well supported all along its stretch, the algorithmmay attempt to fit the best second-degree polynomial (parabola) to thesame original candidate points. The algorithmic difference betweenfinding the best parabola vs. the best straight line is minor: insteadof two unknown coefficients determining the direction and offset of theline there are three coefficients determining the curvature, direction,and offset of the parabola; however, in other respects the process isessentially the same, in one embodiment.

If the support of the parabola is stronger than that of the straightline, especially closer to the ends of the candidate edge span, theconclusion is that the algorithm should prefer the parabola as a bettermodel of the page side in the image. Otherwise, the linear model isemployed, in various approaches.

Intersections of the four found sides of the document may be calculatedin order to find the corners of (possibly slightly curved) pagetetragon, (e.g. tetragon 400 as shown in FIG. 4 and discussed in furtherdetail below). In the preferred implementation in order to do this it isnecessary to consider three cases: calculating intersections of twostraight lines, calculating intersections of a straight line and aparabola, and calculating intersections of two parabolas.

In the first case there is a single solution (since top and bottom pageedges 306 stretch mostly horizontally, while left and right page edges306 stretch mostly vertically, the corresponding LMS lines cannot beparallel) and this solution determines the coordinates of thecorresponding page corner.

The second case, calculating intersections of a straight line and aparabola, is slightly more complicated: there can be zero, one, or twosolutions of the resulting quadratic equation. If there is nointersection, it may indicate a fatal problem with page detection, andits result may be rejected. A single solution is somewhat unlikely, butpresents no further problems. Two intersections present a choice, inwhich case the intersection closer to the corresponding corner of theframe is a better candidate—in practice, the other solution of theequation may be very far away from the coordinate range of the imageframe.

The third case, calculating intersections of two parabolas, results in afourth degree polynomial equation that (in principle) may be solvedanalytically. However, in practice the number of calculations necessaryto achieve a solution may be greater than in an approximate iterativealgorithm that also guarantees the desired sub-pixel precision.

One exemplary procedure used for this purpose is described in detailbelow with reference to rectangularization of the digital representationof the document 302, according to one approach.

There are several constraints on the validity of the resulting targettetragon (e.g. tetragon 400 as discussed in further detail below withregard to FIG. 4). Namely, the tetragon is preferably not too small(e.g., below a predefined threshold of any desired value, such as 25% ofthe total area of the image), the corners of the tetragon preferably donot lie too far outside of the frame of the image (e.g. not more than100 pixels away), and the corners themselves should preferably beinterpretable as top-left, top-right, bottom-left and bottom-right withdiagonals intersecting inside of the tetragon, etc. If these constraintsare not met, a given page detection result may be rejected, in someembodiments.

In one illustrative embodiment where the detected tetragon of thedigital representation of the document 302 is valid, the algorithm maydetermine a target rectangle. Target rectangle width and height may beset to the average of top and bottom sides of the tetragon and theaverage of left and right sides respectively.

In one embodiment, if skew correction is performed, the angle of skew ofthe target rectangle may be set to zero so that the page sides willbecome horizontal and vertical. Otherwise, the skew angle may be set tothe average of the angles of top and bottom sides to the horizontal axisand those of the left and right sides to the vertical axis.

In a similar fashion, if crop correction is not performed, the center ofthe target rectangle may be designated so as to match the average of thecoordinates of the four corners of the tetragon; otherwise the centermay be calculated so that the target rectangle ends up in the top leftof the image frame, in additional embodiments.

In some approaches, if page detection result is rejected for any reason,some or all steps of the process described herein may be repeated with asmaller step increment, in order to obtain more candidate edge pointsand, advantageously, achieve more plausible results. In a worst-casescenario where problems persist even with the minimum allowed step, thedetected page may be set to the whole image frame and the original imagemay be left untouched.

Now with particular reference to an exemplary implementation of theinventive page detection embodiment described herein, in one approachpage detection includes performing a method 1900 such as shown in FIG.19. As will be appreciated by one having ordinary skill in the art uponreading the present descriptions, the method 1900 may be performed inany environment, including those described herein and represented in anyof the Figures provided with the present disclosures.

In one embodiment, method 1900 includes operation 1902, where aplurality of candidate edge points corresponding to a transition from adigital image background to the digital representation of the documentare defined.

In various embodiments, defining the plurality of candidate edge pointsin operation 1902 may include one or more additional operations such asoperations 1904-1920, described below.

In operation 1904, according to one embodiment, a large analysis window(e.g. large analysis window 308 as shown in FIGS. 3A-3B and 3D isdefined within the digital image 300. Preferably, a first large analysiswindow is defined in a region depicting a plurality of pixels of thedigital image background 304, but not depicting the non-background (e.g.the digital representation of the document 302) in order to obtaininformation characteristic of the digital image background 304 forcomparison and contrast to information characteristic of thenon-background (e.g. the digital representation of the document 302,such as background statistics discussed in further detail below withreference to operation 1910). For example, the first large analysiswindow 308 may be defined in a corner (such as a top-left corner) of thedigital image 300. Of course, the first large analysis window may bedefined in any part of the digital image 300 without departing from thescope of the present disclosures.

Moreover, as will be understood by one having ordinary skill in the artupon reading the present descriptions, the large analysis window 308 maybe any size and/or characterized by any suitable dimensions, but inpreferred embodiments the large analysis window 308 is approximatelyforty pixels high and approximately forty pixels wide.

In particularly preferred approaches, the large analysis window 308 maybe defined in a corner region of the digital image. For example, withreference to FIG. 3A, a digital image 300 is shown, the digital image300 comprising a digital representation of a document 302 having aplurality of sides 306 and a background 304. As described above withreference to operation 1904, the large analysis window 308 may bedefined in a region comprising a plurality of background pixels and notincluding pixels corresponding to the digital representation of thedocument 302. Moreover, the large analysis window 308 may be defined inthe corner of the digital image 300, in some approaches.

In operation 1906, according to one embodiment, a plurality of smallanalysis windows 312 may be defined within the digital image 300, suchas within the large analysis window 308. The small analysis windows 312may overlap at least in part with one or more other small analysiswindows 312 such as to be characterized by comprising one or moreoverlap regions 320 as shown in FIG. 3D. In a preferred approach allpossible small analysis windows 312 are defined within the largeanalysis window 308. Of course, small analysis windows may be definedwithin any portion of the digital image, such as shown in FIG. 3B, andpreferably small analysis windows may be defined such that each smallanalysis window is characterized by a single center pixel.

In operation 1908, according to one embodiment, one or more statisticsare calculated for one or more small analysis windows 312 (e.g. one ormore small analysis windows 312 within a large analysis window 308) andone or more distributions of corresponding statistics are estimated(e.g. a distribution of statistics estimated across a plurality of smallanalysis windows 312). In another embodiment, distributions ofstatistics may be estimated across one or more large analysis window(s)308 and optionally merged.

Moreover, values may be descriptive of any feature associated with thebackground of the digital image, such as background brightness values,background color channel values, background texture values, backgroundtint values, background contrast values, background sharpness values,etc. as would be understood by one having ordinary skill in the art uponreading the present descriptions. Moreover still, statistics may includea minimum, a maximum and/or a range of brightness values in one or morecolor channels of the plurality of pixels depicting the digital imagebackground 304 over the plurality of small windows 312 within the largeanalysis window 308.

In operation 1910, according to one embodiment, one or moredistributions of background statistics are estimated. By estimating thedistribution(s) of statistics, one may obtain descriptivedistribution(s) that characterize the properties of the background 304of the digital image 300 within, for example, a large analysis window308.

The distribution(s) preferably correspond to the background statisticscalculated for each small analysis window, and may include, for example,a distribution of brightness minima, a distribution of brightnessmaxima, etc., from which one may obtain distribution statisticaldescriptors such as the minimum and/or maximum of minimum brightnessvalues, the minimum and/or maximum of minimum brightness values, minimumand/or maximum spread of brightness values, minimum and/or maximum ofminimum color channel values, minimum and/or maximum of maximum colorchannel values, minimum and/or maximum spread of color channel valuesetc. as would be appreciated by one having ordinary skill in the artupon reading the present descriptions. Of course, any of the calculatedbackground statistics (e.g. for brightness values, color channel values,contrast values, texture values, tint values, sharpness values, etc.)may be assembled into a distribution and any value descriptive of thedistribution may be employed without departing from the scope of thepresent disclosures.

In operation 1912, according to one embodiment, a large analysis window,such as analysis window 308 as shown in FIGS. 3A-3B is defined withinthe digital image 300.

Moreover, window shapes may be defined by positively setting theboundaries of the window as a portion of the digital image 300, may bedefined by negatively, e.g. by applying a mask to the digital image 300and defining the regions of the digital image 300 not masked as theanalysis window. Moreover still, windows may be defined according to apattern, especially in embodiments where windows are negatively definedby applying a mask to the digital image 300. Of course, other mannersfor defining the windows may be employed without departing from thescope of the present disclosures.

In operation 1914, according to one embodiment, one or more statisticsare calculated for the analysis window 312. Moreover, in preferredembodiments each analysis window statistic corresponds to a distributionof background statistics estimated for the large analysis window 308 inoperation 1910. For example, in one embodiment maximum brightnesscorresponds to distribution of background brightness maxima, minimumbrightness corresponds to distribution of background brightness minima,brightness spread corresponds to distribution of background brightnessspreads, etc. as would be understood by one having ordinary skill in theart upon reading the present descriptions.

In operation 1916, according to one embodiment, it is determined whethera statistically significant difference exists between at least oneanalysis window statistic and the corresponding distribution ofbackground statistics. As will be appreciated by one having ordinaryskill in the art upon reading the present descriptions, determiningwhether a statistically significant difference exists may be performedusing any known statistical significance evaluation method or metric,such as a p-value, a z-test, a chi-squared correlation, etc. as would beappreciated by a skilled artisan reading the present descriptions.

In operation 1918, according to one embodiment, one or more points (e.g.the centermost pixel 318 or point) in the analysis window for which astatistically significant difference exists between a value describingthe pixel 318 and the corresponding distribution of backgroundstatistics is designated as a candidate edge point. The designating maybe accomplished by any suitable method known in the art, such as settinga flag corresponding to the pixel, storing coordinates of the pixel,making an array of pixel coordinates, altering one or more valuesdescribing the pixel 318 (such as brightness, hue, contrast, etc.), orany other suitable means.

In operation 1920, according to one embodiment, one or more ofoperations 1912-1918 may be repeated one or more times. In a preferredembodiment, a plurality of such repetitions may be performed, whereineach repetition is performed on a different portion of the digitalimage. Preferably, the repetitions may be performed until each side ofthe digital representation of the document has been evaluated. Invarious approaches, defining the analysis windows 308, 312 may result ina plurality of analysis windows 308, 312 which share one or moreborders, which overlap in whole or in part, and/or which do not shareany common border and do not overlap, etc. as would be understood by onehaving ordinary skill in the art upon reading the present descriptions.

In a particularly preferred embodiment, the plurality of repetitions maybe performed in a manner directed to reestimate local backgroundstatistics upon detecting a potentially non-background window (e.g. awindow containing a candidate edge point or a window containing anartifact such as uneven illumination, background texture variation,etc.).

In operation 1922, according to one embodiment, four sides of a tetragon400 are defined based on the plurality of candidate edge points.Preferably, the sides of the tetragon 400 encompass the edges 306 of adigital representation of a document 302 in a digital image 300.Defining the sides of the tetragon 400 may include, in some approaches,performing one or more least-mean-squares (LMS) approximations.

In more approaches, defining the sides of the tetragon 400 may includeidentifying one or more outlier candidate edge points, and removing oneor more outlier candidate edge points from the plurality of candidateedge points. Further, defining the sides of the tetragon 400 may includeperforming at least one additional LMS approximation excluding the oneor more outlier candidate edge points.

Further still, in one embodiment each side of the tetragon 400 ischaracterized by an equation chosen from a class of functions, andperforming the at least one LMS approximation comprises determining oneor more coefficients for each equation, such as best coefficients ofsecond degree polynomials in a preferred implementation. According tothese approaches, defining the sides of the tetragon 400 may includedetermining whether each side of the digital representation of thedocument falls within a given class of functions, such as second degreepolynomials or simpler functions such as linear functions instead ofsecond degree polynomials.

In preferred approaches, performing method 1900 may accurately define atetragon around the four dominant sides of a document while ignoring oneor more deviations from the dominant sides of the document, such as arip 310 and/or a tab 320 as depicted in FIGS. 3A-3C and 4.

Additional and/or alternative embodiments of the presently disclosedtetragon 400 may be characterized by having four sides, and each sidebeing characterized by one or more equations such as the polynomialfunctions discussed above. For example, embodiments where the sides oftetragon 400 are characterized by more than one equation may involvedividing one or more sides into a plurality of segments, each segmentbeing characterized by an equation such as the polynomial functionsdiscussed above.

Defining the tetragon 400 may, in various embodiments, alternativelyand/or additionally include defining one or more corners of the tetragon400. For example, tetragon 400 corners may be defined by calculating oneor more intersections between adjacent sides of the tetragon 400, anddesignating an appropriate intersection from the one or more calculatedintersections in cases where multiple intersections are calculated. Instill more embodiments, defining the corners may include solving one ormore equations, wherein each equation is characterized by belonging to achosen class of functions such as N^(th) degree polynomials, etc. aswould be understood by one having ordinary skill in the art upon readingthe present descriptions.

In various embodiments, a corner of the tetragon 400 may be defined byone or more of: an intersection of two curved adjacent sides of thetetragon 400; an intersection of two substantially straight lines; andan intersection of one substantially straight line and one substantiallycurved line.

In operation 1924, according to one embodiment, the digitalrepresentation of the document 302 and the tetragon 400 are output to adisplay of a mobile device. Outputting may be performed in any manner,and may depend upon the configuration of the mobile device hardwareand/or software.

Moreover, outputting may be performed in various approaches so as tofacilitate further processing and/or user interaction with the output.For example, in one embodiment the tetragon 400 may be displayed in amanner designed to distinguish the tetragon 400 from other features ofthe digital image 300, for example by displaying the tetragon 400 sidesin a particular color, pattern, illumination motif, as an animation,etc. as would be understood by one having ordinary skill in the art uponreading the present descriptions.

Further still, in some embodiments outputting the tetragon 400 and thedigital representation of the document 302 may facilitate a usermanually adjusting and/or defining the tetragon 400 in any suitablemanner. For example, a user may interact with the display of the mobiledevice to translate the tetragon 400, i.e. to move the location of thetetragon 400 in one or more directions while maintaining the aspectratio, shape, edge lengths, area, etc. of the tetragon 400. Additionallyand/or alternatively, a user may interact with the display of the mobiledevice to manually define or adjust locations of tetragon 400 corners,e.g. tapping on a tetragon 400 corner and dragging the corner to adesired location within the digital image 300, such as a corner of thedigital representation of the document 302.

Referring again to FIG. 4, one particular example of an ideal result ofpage detection is depicted, showing the digital representation of thedocument 302 within the digital image 300, and having a tetragon 400that encompasses the edges of the digital representation of the document302.

In some approaches page detection such as described above with referenceto FIG. 19 and method 1900 may include one or more additional and/oralternative operations, such as will be described below.

In one approach, method 1900 may further include capturing one or moreof the image data containing the digital representation of the documentand audio data relating to the digital representation of the document.Capturing may be performed using one or more capture components coupledto the mobile device, such as a microphone, a camera, an accelerometer,a sensor, etc. as would be understood by one having ordinary skill inthe art upon reading the present descriptions.

In another approach, method 1900 may include defining a new largeanalysis window 309 and reestimating the distribution of backgroundstatistics for the new large analysis window 309 upon determining thatthe statistically significant difference exists, i.e. essentiallyrepeating operation 1908 and/or 1910 in a different region of thedigital image 300 near a point where a potentially non-background pointhas been identified, such as near one of the edges 306 of the document.

In several exemplary embodiments, a large analysis window 308 may bepositioned near or at the leftmost non-background pixel in a row orpositioned near or at the rightmost non-background pixel in a row,positioned near or at the topmost non-background pixel in a column,positioned near or at bottommost non-background pixel in a column.

Approaches involving such reestimation may further include determiningwhether the statistically significant difference exists between at leastone small analysis window (e.g. a test window) statistic and thecorresponding reestimated distribution of large analysis windowstatistics. In this manner, it is possible to obtain a higher-confidencedetermination of whether the statistically significant differenceexists, and therefore better distinguish true transitions from thedigital image background to the digital representation of the documentas opposed to, for example, variations in texture, illuminationanomalies, and/or other artifacts within the digital image.

Moreover, with or without performing reestimation as described above mayfacilitate the method 1900 avoiding one or more artifacts such asvariations in illumination and/or background texture, etc. in thedigital image, the artifacts not corresponding to a true transition fromthe digital image background to the digital representation of thedocument. In some approaches, avoiding artifacts may take the form ofbypassing one or more regions (e.g. regions characterized by textures,variations, etc. that distinguish the region from the true background)of the digital image.

In some approaches, one or more regions may be bypassed upon determininga statistically significant difference exists between a statisticaldistribution estimated for the large analysis window 308 and acorresponding statistic calculated for the small analysis window 312,defining a new large analysis window near the small analysis window,reestimating the distribution of statistics for the new large analysiswindow, and determining that the statistically significant differencedoes not exist between the reestimated statistical distribution and thecorresponding statistic calculated for the small analysis window 312.

In other approaches, bypassing may be accomplished by checking anotheranalysis window 312 further along the path and resuming the search for atransition to non-background upon determining that the statistics ofthis checked window do not differ significantly from the knownstatistical properties of the background, e.g. as indicated by a test ofstatistical significance.

As will be appreciated by the skilled artisan upon reading the presentdisclosures, bypassing may be accomplished by checking another analysiswindow further along the path.

In still further approaches, page detection may additionally and/oralternatively include determining whether the tetragon 400 satisfies oneor more quality control metrics; and rejecting the tetragon 400 upondetermining the tetragon 400 does not satisfy one or more of the qualitycontrol metrics. Moreover, quality control metrics may include measuressuch as a LMS support metric, a minimum tetragon 400 area metric, atetragon 400 corner location metric, and a tetragon 400 diagonalintersection location metric.

In practice, determining whether the tetragon 400 satisfies one or moreof these metrics acts as a check on the performance of method 1900. Forexample, checks may include determining whether the tetragon 400 coversat least a threshold of the overall digital image area, e.g. whether thetetragon 400 comprises at least 25% of the total image area.Furthermore, checks may include determining whether tetragon 400diagonals intersect inside the boundaries of the tetragon 400,determining whether one or more of the LMS approximations werecalculated from sufficient data to have robust confidence in thestatistics derived therefrom, i.e. whether the LMS approximation hassufficient “support,” (such as an approximation calculated from at leastfive data points, or at least a quarter of the total number of datapoints, in various approaches), and/or determining whether tetragon 400corner locations (as defined by equations characterizing each respectiveside of the tetragon 400) exist within a threshold distance of the edgeof the digital image, e.g. whether tetragon 400 corners are located morethan 100 pixels away from an edge of the digital image in a givendirection. Of course, other quality metrics and/or checks may beemployed without departing from the scope of these disclosures, as wouldbe appreciated by one having ordinary skill in the art upon reading thepresent descriptions.

In one approach, quality metrics and/or checks may facilitate rejectingsuboptimal tetragon 400 definitions, and further facilitate improvingthe definition of the tetragon 400 sides. For example, one approachinvolves receiving an indication that the defining the four sides of thetetragon 400 based on the plurality of candidate edge points failed todefine a valid tetragon 400, i.e. failed to satisfy one or more of thequality control metrics; and redefining the plurality of candidate edgepoints. Notably, in this embodiment redefining the plurality ofcandidate edge points includes sampling a greater number of pointswithin the digital image than a number of points sampled in the prior,failed attempt. This may be accomplished, in one approach, by reducingthe step over one or more of rows or columns of the digital image andrepeating all the steps of the algorithm in order to analyze a largernumber of candidate edge points. The step may be decreased in a verticaldirection, a horizontal direction, or both. Of course, other methods ofredefining the candidate edge points and/or resampling points within thedigital image may be utilized without departing from the scope of thepresent disclosures.

Further still, page detection may include designating the entire digitalimage as the digital representation of the document, particularly wheremultiple repetitions of method 1900 failed to define a valid tetragon400, even with significantly reduced step in progression through thedigital image analysis. In one approach, designating the entire digitalimage as the digital representation of the document may include definingimage corners as document corners, defining image sides as documentsides, etc. as would be understood by one having ordinary skill in theart upon reading the present descriptions.

As described herein, the diagonals of the tetragon 400 may becharacterized by a first line connecting a calculated top left corner ofthe tetragon 400 to a calculated bottom right corner of the tetragon400, and second line connecting a calculated top right corner of thetetragon 400 and a calculated bottom left corner of the tetragon 400.Moreover, the first line and the second line preferably intersect insidethe tetragon 400.

In various approaches, one or more of the foregoing operations may beperformed using a processor, and the processor may be part of a mobiledevice, particularly a mobile device having an integrated camera.

Various Embodiments of Mobile Page Rectangularization

The present descriptions relate to rectangularizing a digitalrepresentation of a document in a digital image, various approaches towhich will be described in detail below with reference to FIGS. 5A-5Cand 20.

In one embodiment, the goal of a rectangularization algorithm is tosmoothly transform a tetragon 400 (such as defined above in pagedetection method 1900) into a rectangle (such as shown in FIG. 5C).Notably, the tetragon 400 is characterized by a plurality of equations,each equation corresponding to a side of the tetragon 400 and beingselected from a chosen class of functions. For example, each side of thetetragon 400 may be characterized by a first degree polynomial, seconddegree polynomial, third degree polynomial, etc. as would be appreciatedby the skilled artisan upon reading the present descriptions.

In one approach, sides of the tetragon 400 may be described byequations, and in a preferred embodiment a left side of the tetragon 400is characterized by a second degree polynomial equation:x=a₂*y²+a₁*y+a₀; a right side of the tetragon 400 is characterized by asecond degree polynomial equation: x=b₂*y²+b₁*y+b₀; a top side of thetetragon 400 is characterized by a second degree polynomial equation:y=c₂*x²+c₁*x+c₀; and a bottom side of the tetragon 400 is characterizedby a second degree polynomial equation: y=d₂*x²+d₁*x+d₀.

The description of page rectangularization algorithm presented belowutilizes the definition of a plurality of tetragon-based intrinsiccoordinate pairs (p, q) within the tetragon, each intrinsic coordinatepair (p, q) corresponding to an intersection of a top-to-bottom curvecharacterized by an equation obtained from the equations of its left andright sides by combining all corresponding coefficients in atop-to-bottom curve coefficient ratio of p to 1−p, and a left-to-rightcurve characterized by an equation obtained from the equations of itstop and bottom sides by combining all corresponding coefficients in aleft-to-right curve coefficient ratio of q to 1−q, wherein 0≦p≦1, andwherein 0≦q≦1.

In a preferred embodiment where the sides of the tetragon 400 arecharacterized by second degree polynomial equations, the top-to-bottomcurve corresponding to the intrinsic coordinate p will be characterizedby the equation: x=((1−p)*a₂+p*b₂)*y²+((1−p)*a₁+p*b₁)*y+((1−p)*a₀+p*b₀),and the left-to-right curve corresponding to the intrinsic coordinate qwill be characterized by the equation:y=((1−q)*c₂+q*d₂)*y²+((1−q)*c₁+q*d₁)*y+((1−q)*c₀+q*d₀). Of course, otherequations may characterize any of the sides and/or curves describedabove, as would be appreciated by one having ordinary skill in the artupon reading the present descriptions.

For a rectangle, which is a particular case of a tetragon, the intrinsiccoordinates become especially simple: within the rectangle, eachintrinsic coordinate pair (p, q) corresponds to an intersection of aline parallel to each of a left side of the rectangle and a right sideof the rectangle, e.g. a line splitting both top and bottom sides in theproportion of p to 1−p; and a line parallel to each of a top side of therectangle and a bottom side of the rectangle, e.g. a line splitting bothtop and bottom sides in the proportion of q to 1−q, wherein 0≦p≦1, andwherein 0≦q≦1.

The goal of the rectangularization algorithm described below is to matcheach point in the rectangularized image to a corresponding point in theoriginal image, and do it in such a way as to transform each of the foursides of the tetragon 400 into a substantially straight line, whileopposite sides of the tetragon 400 should become parallel to each otherand orthogonal to the other pair of sides; i.e. top and bottom sides ofthe tetragon 400 become parallel to each other; and left and right sidesof the tetragon 400 become parallel to each other and orthogonal to thenew top and bottom. Thus, the tetragon 400 is transformed into a truerectangle characterized by four corners, each corner comprising twostraight lines intersecting to form a ninety-degree angle.

The main idea of the rectangularization algorithm described below is toachieve this goal by, first, calculating rectangle-based intrinsiccoordinates (p, q) for each point P (not shown) in the rectangularizeddestination image, second, matching these to the same pair (p, q) oftetragon-based intrinsic coordinates in the original image, third,calculating the coordinates of the intersection of the left-to-right andtop-to-bottom curves corresponding to these intrinsic coordinatesrespectively, and finally, assigning the color or gray value at thefound point in the original image to the point P.

Referring now to FIG. 5A, which depicts a graphical representation of afirst iteration of a page rectangularization algorithm, according to oneembodiment. As shown in FIG. 5A, each point in a digital image 500 maycorrespond to an intersection of a top-to-bottom curve 504 and aleft-to-right curve 506 (a curve may include a straight line, a curvedline, e.g. a parabola, etc. as would be understood by one havingordinary skill in the art upon reading the present descriptions)corresponding to intrinsic coordinates (such as described above)associated with a point.

As will become apparent from the present descriptions,rectangularization may involve defining a plurality of suchleft-to-right lines 506 and top-to-bottom lines 504.

Moreover, rectangularization may include matching target rectangle-basedcoordinates to intrinsic tetragon-based coordinates of the digitalrepresentation of the document 502.

As shown in FIG. 5A, this matching may include iteratively searching foran intersection of a given left-to-right curve 506 and a giventop-to-bottom curve 504. FIG. 5A shows the first iteration of anexemplary iterative search within the scope of the present disclosures.

The iterative search, according to one approach discussed in furtherdetail below with regard to FIG. 20, includes designating a startingpoint 508 having coordinates (x₀, y₀), The starting point 508 may belocated anywhere within the digital representation of the document 502,but preferably is located at or near the center of the target rectangle.

The iterative search may include projecting the starting point 508 ontoone of the two intersecting curves 504, 506. While the starting pointmay be projected onto either of the curves 504, 506, in one approach thefirst half of a first iteration in the iterative search includesprojecting the starting point 508 onto the top-to-bottom curve to obtainx-coordinate (x₁) of the next point, the projection result representedin FIG. 5A by point 510, which has coordinates (x₁, y₀). Similarly, insome embodiments the second half of a first iteration in the iterativesearch includes projecting the point 510 onto the left-to-right curve506 to obtain y-coordinate (y₁) of the next point, the projection resultrepresented in FIG. 5A by point 512, which has coordinates (x₁, y₁).

FIG. 5B is a graphical representation of a starting point of a pagerectangularization algorithm, after dividing the digital representationof the document 502 into a plurality of equally-sized sections definedby the plurality of top-to-bottom curves 504 and the plurality ofleft-to-right curves 506, according to one embodiment.

Rectangularization involves transforming the tetragon 400 defined inpage detection into a true rectangle. The result of this process isshown in FIG. 5C as a graphical representation of an output afterperforming a page rectangularization algorithm, according to oneembodiment.

Further iterations may utilize a similar approach such as described infurther detail below with respect to FIG. 20 and method 2000, in someembodiments.

With continuing reference to FIGS. 5A-5C, and now with additionalreference to FIG. 20, a method 2000 for modifying one or more spatialcharacteristics of a digital representation of a document in a digitalimage is shown, according to one embodiment. As will be appreciated byone having ordinary skill in the art upon reading the presentdescriptions, method 2000 may be performed in any suitable environment,including those shown and/or described in the figures and correspondingdescriptions of the present disclosures.

In one embodiment, method 2000 includes operation 2002, where a tetragon400 (such as defined above in page detection method 1900) is transformedinto a rectangle (such as shown in FIG. 5C). Notably, the tetragon 400is characterized by a plurality of equations, each equationcorresponding to a side of the tetragon 400 and being selected from achosen class of functions. For example, each side of the tetragon 400may be characterized by a first degree polynomial, second degreepolynomial, third degree polynomial, etc. as would be appreciated by theskilled artisan upon reading the present descriptions.

In one embodiment, sides of the tetragon 400 may be described byequations, and in a preferred embodiment a left side of the tetragon 400is characterized by a second degree polynomial equation:x=a₂*y²+a₁*y+a₀; a right side of the tetragon 400 is characterized by asecond degree polynomial equation: x=b₂*y²+b₁*y+b₀; a top side of thetetragon 400 is characterized by a second degree polynomial equation:y=c₂*x²+c₁*x+c₀; and a bottom side of the tetragon 400 is characterizedby a second degree polynomial equation: y=d₂*x²+d₁*x+d₀. Moreover, thetop-to-bottom curve equation is:x=((1−p)*a₂+p*b₂)*y²+((1−p)*a₁+p*b₁)*y+((1−p)*a₀+p*b₀), and theleft-to-right curve equation is:y=((1−q)*c₂+q*d₂)*y²+((1−q)*c₁+q*d₁)*y+((1−q)*c₀+q*d₀). Of course, otherequations may characterize any of the sides and/or curves describedabove, as would be appreciated by one having ordinary skill in the artupon reading the present descriptions.

In one embodiment, curves 504, 506 may be described by exemplarypolynomial functions fitting one or more of the following general formsx ₁ =u ₂ *y ₀ ² +u ₁ *y ₀ +u ₀;y ₁ =v ₂ *x ₁ ² +v ₁ *x ₁ +v ₀,where u_(i)=(1−p)*a_(i)+p*b_(i), and v_(i)=(1−q)*c_(i)+q*d_(i), andwhere, a_(i) are the coefficients in the equation of the left side ofthe tetragon, b_(i) are the coefficients in the equation of the rightside of the tetragon, c_(i) are the coefficients in the equation of thetop side of the tetragon, d_(i) are the coefficients in the equation ofthe bottom side of the tetragon, and p and q are the tetragon-basedintrinsic coordinates corresponding to curves 504, 506. In someapproaches, the coefficients such as a_(i), b_(i), c_(i), d_(i), etc.may be derived from calculations, estimations, and/or determinationsachieved in the course of performing page detection, such as a pagedetection method as discussed above with reference to method 1900 andFIG. 19.

Of course, as would be understood by one having ordinary skill in theart, transforming the tetragon 400 into a rectangle may include one ormore additional operations, such as will be described in greater detailbelow.

In one embodiment, method 2000 additionally and/or alternativelyincludes stretching one or more regions of the tetragon 400 to achieve amore rectangular or truly rectangular shape. Preferably, such stretchingis performed in a manner sufficiently smooth to avoid introducingartifacts into the rectangle.

In some approaches, transforming the tetragon 400 into a rectangle mayinclude determining a height of the rectangle, a width of the rectangle,a skew angle of the rectangle, and/or a center position of therectangle. For example, such transforming may include defining a widthof the target rectangle as the average of the width of the top side andthe width of the bottom side of the tetragon 400; defining a height ofthe target rectangle as the average of the height of the left side andthe height of the right side of the tetragon 400; defining a center ofthe target rectangle depending on the desired placement of the rectanglein the image; and defining an angle of skew of the target rectangle,e.g. in response to a user request to deskew the digital representationof the document.

In some approaches, the transforming may additionally and/oralternatively include generating a rectangularized digital image fromthe original digital image; determining a p-coordinate and aq-coordinate for a plurality of points within the rectangularizeddigital image (e.g. points both inside and outside of the targetrectangle) wherein each point located to the left of the rectangle has ap-coordinate value p<0, wherein each point located to the right of therectangle has a p-coordinate value p>1, wherein each point located abovethe rectangle has a q-coordinate value q<0, and wherein each pointlocated below the rectangle has a q-coordinate value q>1.

In some approaches, the transforming may additionally and/oralternatively include generating a rectangularized digital image fromthe original digital image; determining a pair of rectangle-basedintrinsic coordinates for each point within the rectangularized digitalimage; and matching each pair of rectangle-based intrinsic coordinatesto an equivalent pair of tetragon-based intrinsic coordinates within theoriginal digital image.

In preferred approaches, matching the rectangle-based intrinsiccoordinates to the tetragon-based intrinsic coordinates may include:performing an iterative search for an intersection of the top-to-bottomcurve and the left-to-right curve. Moreover, the iterative search mayitself include designating a starting point (x₀, y₀), for example, thecenter of the target rectangle; projecting the starting point (x₀, y₀)onto the left-to-right curve: x₁=u₂*y₀ ²+y₀+u₀; and projecting a nextpoint (x₁, y₀) onto the top-to-bottom curve: y₁=v₂*x₁ ²+v₁*x₁+v₀, whereu_(i)=(1−p)*a_(i)+p*b_(i), and where v_(i)=(1−q)*c_(i)+q*d_(i).Thereafter, the iterative search may include iteratively projecting(x_(k), y_(k)) onto the left-to-right curve: x_(k+1)=u₂*y_(k)²+u₁*y_(k)+u₀; and projecting (x_(k+1), y_(k)) onto the top-to-bottomcurve: y_(k+1)=v₂*x_(k+1) ²+v₁*x_(k+1)+v₀.

In still more embodiments, matching the rectangle-based intrinsiccoordinates to the tetragon-based intrinsic coordinates may includedetermining a distance between (x_(k), y_(k)) and (x_(k+1), y_(k+1));determining whether the distance is less than a predetermined threshold;and terminating the iterative search upon determining that the distanceis less than the predetermined threshold.

Various Embodiments of Page Skew Correction

In some embodiments, the image processing algorithm disclosed herein mayadditionally and/or alternatively include functionality designed todetect and/or correct a skew angle of a digital representation of adocument in a digital image. One preferred approach to correcting skeware described below with reference to FIG. 6. Of course, other methodsof correcting skew within a digital image are within the scope of thethese disclosures, as would be appreciated by one having ordinary skillin the art upon reading the present descriptions.

FIG. 6 is a graphical representation of one algorithmic approach todetecting and/or correcting skew of a digital representation of adocument 602 in a digital image, according to one embodiment.

As shown in FIG. 6, a digital representation of a document 602 in adigital image may be characterized by one or more skew angles α As willbe appreciated by the skilled artisan reading these descriptions andviewing FIG. 6, horizontal skew angle α represents an angle between ahorizontal line 612 and an edge 604, 606 of the digital representationof the document, the edge 604, 606 having its longitudinal axis in asubstantially horizontal direction (i.e. either the top or bottom edgeof the digital representation of the document 602). Similarly, a mayrepresent an angle between a vertical line 614 and an edge 608, 610 ofthe digital representation of the document, the edge 608, 610 having itslongitudinal axis in a substantially vertical direction (i.e. either theleft edge 608 or right edge 610 of the digital representation of thedocument 602).

Moreover, as further shown in FIG. 6, the digital representation of thedocument 602 may be defined by a top edge 604, a bottom edge 606, aright edge 610 and a left edge 608. Each of these edges may becharacterized by a substantially linear equation, such that for top edge604: y=−tan(α)x+dt; for bottom edge 606: y=−tan(α)x+db; for right edge610: x=tan(α)y+dr; and for left edge 608: x=tan(α)y+dl, where dt and dbare the y-intercept of the linear equation describing the top and bottomedges of the digital representation of the document, respectively, andwhere dr and dl are the x-intercept of the linear equation describingthe right and left edges of the digital representation of the document,respectively.

In one approach, having defined the linear equations describing eachside of the digital representation of the document 602, for example arectangular document, a skew angle thereof may be corrected by settingα=0, such that for top edge 604: y=dt; for bottom edge 606: y=db; forright edge 610: x=dr; and for left edge 608: x=dl.

Various Embodiments of Mobile Page Illumination Detection

In still more embodiments, the presently described image processingalgorithm may include features directed to detecting whether a digitalrepresentation of a document comprises one or more illuminationproblems.

For example, illumination problems may include locally under-saturatedregions of a digital image, when brightness values vary greatly frompixel-to-pixel within image backgrounds, such as is characteristic ofimages captured in settings with insufficient ambient and/or providedillumination, and locally over-saturated regions of a digital image,when some areas within the image are washed out, such as within thereflection of the flash.

One exemplary approach to detecting illumination problems in a digitalimage including a digital representation of a document are describedbelow with reference to FIG. 7A, which is a pictorial representation ofa digital image 700 comprising a digital representation of a document702 characterized by an illumination problem 704, according to oneembodiment; and FIG. 21, which depicts a method 2100 for determiningwhether illumination problems exist in a digital representation of adocument. As will be appreciated by one having ordinary skill in the artupon reading the present descriptions, method 2100 may be performed inany suitable environment, such as those described herein and representedin the various Figures submitted herewith. Of course, other environmentsmay also be suitable for operating method 2100 within the scope of thepresent disclosures, as would be appreciated by the skilled artisanreading the instant specification.

In one embodiment, method 2100 includes operation 2102, which involvesdividing, using a processor, a tetragon 400 including a digitalrepresentation of a document into a plurality of sections, each sectioncomprising a plurality of pixels.

In more approaches, method 2100 includes operation 2104, where adistribution of brightness values of each section is determined. As willbe understood by one having ordinary skill in the art, the distributionof brightness values may be compiled and/or assembled in any knownmanner, and may be fit to any known standard distribution model, such asa Gaussian distribution, a bimodal distribution, a skewed distribution,etc.

In still more approaches, method 2100 includes operation 2106, where abrightness value range of each section is determined. As will beappreciated by one having ordinary skill in the art, a range is definedas a difference between a maximum value and a minimum value in a givendistribution. Here the brightness value range would be defined as thedifference between the characteristic maximum brightness value in agiven section and the characteristic minimum brightness value in thesame section. For example, these characteristic values may correspond tothe 2′¹ and 98^(th) percentiles of the whole distribution respectively.

In many approaches, method 2100 includes operation 2108, where avariability of brightness values of each section is determined.

In various approaches, method 2100 includes operation 2110, where it isdetermined whether each section is oversaturated. For example, operation2112 may include determining that a region 704 of a digital image 700depicting a digital representation of a document 702 is oversaturated,as shown in FIG. 7A according to one embodiment. Determining whethereach section is oversaturated may include determining a sectionoversaturation ratio for each section. Notably, in preferred embodimentseach section oversaturation ratio is defined as a number of pixelsexhibiting a maximum brightness value in the section divided by a totalnumber of pixels in the section.

As shown in FIG. 7A, an unevenly illuminated image may depict or becharacterized by by a plurality of dark spots 708 that may be more densein areas where the brightness level of a corresponding pixel, point orregion of the digital image is lower than that of other regions of theimage or document, and/or lower than an average brightness level of theimage or document. In some embodiments, uneven illumination may becharacterized by a brightness gradient, such as shown in FIG. 7A with agradient proceeding from a top right corner of the image (near region706) to a lower left corner of the image (near region 704) such thatbrightness decreases along the gradient with a relatively bright area inthe top right corner of the image (near region 706) and a relativelydark area in the lower left corner of the image (near region 704).

In some approaches, determining whether each section is oversaturatedmay further include determining, for each section, whether theoversaturation level of the section is greater than a predeterminedthreshold, such as 10%; and characterizing the section as oversaturatedupon determining that the saturation level of the section is greaterthan the predetermined threshold. While the presently describedembodiment, employs a threshold value of 10%, other predeterminedthreshold oversaturation levels may be employed without departing fromthe scope of the present descriptions. Notably, the exact value is amatter of visual perception and expert judgment, and may be adjustedand/or set by a user in various approaches.

In more approaches, method 2100 includes operation 2112, where it isdetermined whether each section is undersaturated. For example,operation 2112 may include determining that a region 704 of a digitalimage 700 depicting a digital representation of a document 702 isundersaturated, as shown in FIG. 7A according to one embodiment.Determining whether each section is under-saturated may includeadditional operations such as determining a median variability of thedistribution of brightness values of each section; determining whethereach median variability is greater than a predetermined variabilitythreshold, such as a median brightness variability of 18 out of a 0-255integer value range; and determining, for each section, that the sectionis undersaturated upon determining that the median variability of thesection is greater than the predetermined variability threshold.Notably, the exact value is a matter of visual perception and expertjudgment, and may be adjusted and/or set by a user in variousapproaches.

In one particular approach, determining the variability of the sectionmay include determining a brightness value of a target pixel in theplurality of pixels; calculating a difference between the brightnessvalue of the target pixel and a brightness value for one or moreneighboring pixels, each neighboring pixel being one or more (forexample, 2) pixels away from the target pixel; repeating the determiningand the calculating for each pixel in the plurality of pixels to obtaineach target pixel variability; and generating a distribution of targetpixel variability values, wherein each target pixel brightness value andtarget pixel variability value is an integer in a range from 0 to 255.This approach may be implemented, for example, by incrementing acorresponding counter in an array of all possible variability values ina range from 0 to 255, e.g. to generate a histogram of variabilityvalues.

Notably, when utilizing neighboring pixels in determining thevariability of a particular section, the neighboring pixels may bewithin about two pixels of the target pixel along either a verticaldirection, a horizontal direction, or both (e.g. a diagonal direction).Of course, other pixel proximity limits may be employed withoutdeparting from the scope of the present invention.

In some approaches, method 2100 may further include removing one or moretarget pixel variability values from the distribution of target pixelvariability values to generate a corrected distribution; and defining acharacteristic background variability based on the correcteddistribution. For example, in one embodiment generating a correcteddistribution and defining the characteristic background variability mayinclude removing the top 35% of total counted values (or any other valuesufficient to cover significant brightness changes associated withtransitions from the background to the foreground) and define thecharacteristic background variability based on the remaining values ofthe distribution, i.e. values taken from a relatively flat backgroundregion of the digital representation of the document.

In more approaches, method 2100 includes operation 2114, where a numberof oversaturated sections is determined. This operation may include anymanner of determining a total number of oversaturated sections, e.g. byincrementing a counter during processing of the image, by setting a flagfor each oversaturated section and counting flags at some point duringprocessing, etc. as would be understood by one having ordinary skill inthe art upon reading the present descriptions.

In more approaches, method 2100 includes operation 2116, where a numberof undersaturated sections is determined. This operation may include anymanner of determining a total number of undersaturated sections, e.g. byincrementing a counter during processing of the image, by setting a flagfor each undersaturated section and counting flags at some point duringprocessing, etc. as would be understood by one having ordinary skill inthe art upon reading the present descriptions.

In more approaches, method 2100 includes operation 2118, where it isdetermined that the digital image is oversaturated upon determining thata ratio of the number of oversaturated sections to the total number ofsections exceeds an oversaturation threshold, which may be defined by auser, may be a predetermined value, etc. as would be understood by onehaving ordinary skill in the art upon reading the present descriptions.

In more approaches, method 2100 includes operation 2120, where it isdetermined that the digital image is undersaturated upon determiningthat a ratio of the number of undersaturated sections to the totalnumber of sections exceeds an undersaturation threshold, which may bedefined by a user, may be a predetermined value, etc. as would beunderstood by one having ordinary skill in the art upon reading thepresent descriptions.

In more approaches, method 2100 includes operation 2122, where it isdetermined that the illumination problem exists in the digital imageupon determining that the digital image is either undersaturated oroversaturated.

In still more approaches, method 2100 may include one or more additionaland/or alternative operations, such as will be described in detailbelow.

In one embodiment, method 2100 may include performing the followingoperations, for each section. Defining a section height by dividing theheight of the document into a predefined number of horizontal sections;and defining a section width by dividing the width of the document intoa predetermined number of vertical sections. In a preferred approach,the section height and width are determined based on the goal ofcreating a certain number of sections and making these sectionsapproximately square by dividing the height of the document into acertain number of horizontal parts and by dividing the width of thedocument into a certain (possibly different) number of vertical parts.

Thus, with reference to FIG. 7A and method 2100, in some embodimentseach section is characterized by a section height and width, where thedigital image is characterized by an image width w and an image heighth, where h>=w, where the section size is characterized by a sectionwidth w_(s) and a section height h_(s) where w_(s)=w/m, where h_(s)=h/n,where m and n are defined so that w_(s) is approximately equal to h_(s).For example, in a preferred embodiment, m>=3, n>=4.

In another approach, a method 2200 for determining whether illuminationproblems exist in a digital representation of a document, includes thefollowing operations, some or all of which may be performed in anyenvironment described herein and/or represented in the presentlydisclosed figures.

Various Embodiments of Mobile Page Illumination Normalization

FIG. 7B is a pictorial representation an output of the digital image 700as shown in FIG. 7A after correcting a detected unevenness ofillumination in a digital image, 700, according to one embodiment. Insome approaches, correcting unevenness of illumination in a digitalimage 700 includes normalizing an overall brightness level of thedigital image. With reference to FIG. 7A, normalizing overall brightnessmay transform a digital image characterized by a brightness gradientsuch as discussed above and shown in FIG. 7A into a digital imagecharacterized by a relatively flat, even distribution of brightnessacross the digital image, such as shown in FIG. 7B. Note that in FIG. 7Aregion 704 is characterized by a significantly more dense distributionof dark spots 708 than region 706, but in FIG. 7B regions 704, 706 arecharacterized by substantially similar dark spot 708 density profiles.

In accordance with the present disclosures, unevenness of illuminationmay be corrected. In particular, a method 2200 for correcting unevenillumination in one or more regions of the digital image is providedherein for use in any suitable environment, including those describedherein and represented in the various figures, among other suitableenvironments as would be known by one having ordinary skill in the artupon reading the present descriptions.

In one embodiment, method 2200 includes operation 2202 where, using aprocessor, a two-dimensional illumination model is derived from thedigital image.

In one embodiment, method 2200 includes operation 2204, where thetwo-dimensional illumination model is applied to each pixel in thedigital image.

In more approaches, the digital image may be divided into a plurality ofsections, and some or all of the pixels within a section may beclustered based on color, e.g. brightness values in one or more colorchannels, median hue values, etc. as would be understood by one havingordinary skill in the art upon reading the present descriptions.Moreover, several most numerous clusters may be analyzed to determinecharacteristics of one or more possible local backgrounds. In order todesignate a cluster as a local background of the section, the number ofpixels belonging to this cluster has to exceed a certain predefinedthreshold, such as a threshold percentage of the total section area.

In various approaches, clustering may be performed using any knownmethod, including Markov-chain Monte Carlo methods, nearest neighborjoining, distribution-based clustering such as expectation-maximization,density-based clustering such as density-based spatial clustering ofapplications with noise (DBSCAN), ordering points to identify theclustering structure (OPTICS), etc. as would be understood by one havingordinary skill in the art upon reading the present descriptions.

In one embodiment, method 2200 may include determining, for eachdistribution of color channel values within background clusters, one ormore of an average color of the primary background of the correspondingsection and an average color of the secondary background of thecorresponding section, if one or both exist in the section.

In one embodiment, method 2200 includes designating, for each section,either the primary background color or the secondary background color asa local representation of a main background of the digitalrepresentation of the document, each local representation beingcharacterized by either the average color of the primary background ofthe corresponding section or the average color of the secondarybackground of the corresponding section;

In one embodiment, method 2200 includes fitting a plurality of averagecolor channel values of chosen local representations of the imagebackground to a two-dimensional illumination model. In some approaches,the two-dimensional illumination model is a second-degree polynomialcharacterized by the equation: v=ax²+bxy+cy²+dx+ey+f; where v is anaverage color channel value for one of the plurality of color channels;a, b, c, d, e, and f are each unknown parameters of the two-dimensionalillumination model, each unknown parameter a, b, c, d, e, and f isapproximated using a least-mean-squares approximation, x is ax-coordinate of the mid-point pixel in the section, and y is ay-coordinate of the mid-point pixel in the section.

In one approach, derivation of the two-dimensional illumination modelmay include, for a plurality of background clusters: calculating anaverage color channel value of each background cluster, calculating ahue ratio of each background cluster, and calculating a median hue ratiofor the plurality of background clusters. Moreover, the derivation mayalso include comparing the hue ratio of each background cluster to themedian hue ratio of the plurality of clusters; selecting the more likelyof the possible two backgrounds as the local representation of thedocument background based on the comparison; fitting at least onetwo-dimensional illumination model to the average channel values of thelocal representation; and calculating a plurality of average mainbackground color channel values over a plurality of localrepresentations.

The applying of the model may include the calculating of a differencebetween one or more predicted background channel values and the averagemain background color channel values; and adding a fraction of thedifference to one or more color channel values for each pixel in thedigital image. For example, adding the fraction may involve adding avalue in a range from 0 to 1 of the difference, for example, ¾ of thedifference, in a preferred embodiment, to the actual pixel value.

In still more approaches, method 2200 may include additional and/oralternative operations, such as those discussed immediately below withcontinuing reference to FIG. 22.

For example, in one approach method 2200 further includes one or moreof: determining, for each section, a plurality of color clusters;determining a plurality of numerous color clusters, each numerous colorcluster corresponding to high frequency of representation in the section(e.g. the color cluster is one of the clusters with the highest numberof pixels in the section belonging to that color cluster) determining atotal area of the section; determining a plurality of partial sectionareas, each partial section area corresponding to an area represented byone the plurality of numerous color clusters; dividing each partialsection area by the total area to obtain a cluster percentage area foreach numerous color cluster; (e.g. by dividing the number of pixels inthe section belonging to numerous color clusters by the total number ofpixels in the section to obtain a percentage of a total area of thesection occupied by the corresponding most numerous color clusters) andclassifying each numerous color cluster as either a background clusteror a non-background cluster based on the cluster percentage area.

Notably, in preferred approaches the classifying operation identifieseither: no background in the section, a single most numerous backgroundin the section, or two most numerous backgrounds in the section.Moreover, the classifying includes classifying each belonging to acluster containing a number of pixels greater than a backgroundthreshold as a background pixel. In some approaches, the backgroundthreshold is in a range from 0 to 100% (for example, 15% in a preferredapproach). The background threshold may be defined by a user, may be apredetermined value, etc. as would be understood by one having ordinaryskill in the art upon reading the present descriptions.

Various Embodiments of Mobile Page Resolution Estimation and DocumentClassification

As a further object of the presently disclosed inventive embodiments,mobile image processing may include a method 2300 for estimatingresolution of a digital representation of a document. Of course, method2300 may be performed in any suitable environment, including thosedescribed herein and represented in the various figures presentedherewith. Moreover, method 2300 may be used in conjunction with anyother method described herein, and may include additional and/oralternative operations to those described below, as would be understoodby one having ordinary skill in the art upon reading the presentdescriptions.

In one embodiment, method 2300 includes operation 2302, where aplurality of connected components of a plurality of non-backgroundelements are detected in the digital image. In some approaches, thedigital image may be characterized as a bitonal image, i.e. an imagecontaining only two tones, and preferably a black and white image.

In another embodiment, method 2300 includes operation 2304, where aplurality of likely characters is determined based on the plurality ofconnected components. Likely characters may be regions of a digitalimage characterized by a predetermined number of light-to-darktransitions in a given direction, such as three light-to-darktransitions in a vertical direction as would be encountered for a smallregion of the digital image depicting a capital letter “E,” eachlight-to-dark transition corresponding to a transition from a backgroundof a document (light) to one of the horizontal strokes of the letter“E.” Of course, other numbers of light-to-dark transitions may beemployed, such as two vertical and/or horizontal light-to-darktransitions for a letter “o,” one vertical light to dark transition fora letter “1,” etc. as would be understood by one having ordinary skillin the art upon reading the present descriptions.

In still another embodiment, method 2300 includes operation 2306, whereone or more average character dimensions are determined based on theplurality of likely text characters. As understood herein, the averagecharacter dimensions may include one or more of an average characterwidth and an average character height, but of course other suitablecharacter dimensions may be utilized, as would be recognized by askilled artisan reading the present descriptions.

In still yet another embodiment, method 2300 includes operation 2308,where the resolution of the digital image is estimated based on the oneor more average character dimensions.

In further embodiments, method 2300 may optionally and/or alternativelyinclude one or more additional operations, such as described below withcontinuing reference to FIG. 23.

For example, in one embodiment method 2300 may further include one ormore of: estimating one or more dimensions of the digital representationof the document based on the estimated resolution of the digital image;comparing the one or more estimated dimensions of the digitalrepresentation of the document to one or more known dimensions of aplurality of known document types; matching the digital representationof the document to one or more of the plurality of known document typesbased on the comparison; determining whether the match satisfies one ormore quality control criteria; and adjusting the estimated resolution ofthe digital representation of the document based on the known dimensionsof the known document type upon determining the match satisfies the oneor more quality control criteria. In some approaches, the estimatedresolution will only be adjusted if a good match between the digitalrepresentation of the document and one of the known document types hasbeen found.

In some approaches, the one or more known document types include: aLetter size document (8.5×11 inch); a Legal size document (8.5×14 inch);an A3 document (11.69×16.54 inch); an A4 (European Letter size) document(8.27×11.69 inch); an A5 document (5.83×8.27 inch); a ledger/tabloiddocument (11×17 inch); a driver license (2.125×3.375 inch); a businesscard (2×3.5 inch); a personal check (2.75×6 inch); a business check(3×7.25 inch); a business check (3×8.25 inch); a business check(2.75×8.5 inch); a business check (3.5×8.5 inch); a business check(3.66×8.5 inch); a business check (4×8.5 inch); a 2.25-inch widereceipt; and a 3.125-inch wide receipt.

In still more approaches, method 2300 may further and/or optionallyinclude computing, for one or more connected components, one or more of:a number of on-off transitions within the connected component; (forexample transitions from a character to a document background, e.g.transitions from black-to-white, white-to-black, etc. as would beunderstood by the skilled artisan reading the present descriptions); ablack pixel density within the connected component; an aspect ratio ofthe connected component; and a likelihood that one or more of theconnected components represents a text character based on one or more ofthe black pixel density, the number of on-off transitions, and theaspect ratio.

In still more approaches, method 2300 may further and/or optionallyinclude determining a character height of at least two of the pluralityof text characters; calculating an average character height based oneach character height of the at least two text characters; determining acharacter width of at least two of the plurality of text characters;calculating an average character width based on each character width ofthe at least two text characters; performing at least one comparison.Notably, the comparison may be selected from: comparing the averagecharacter height to a reference average character height; and comparingthe average character width to a reference average character width.

In such approaches, method 2300 may further include estimating theresolution of the digital image based on the at least one comparison,where each of the reference average character height and the referenceaverage character width correspond to one or more reference characters,each reference character being characterized by a known averagecharacter width and a known average character height.

In various embodiments, each reference character corresponds to adigital representation of a character obtained from scanning arepresentative sample of one or more business document(s) at someselected resolution, such as 300 DPI, and each reference characterfurther corresponds to one or more common fonts, such as Arial, TimesNew Roman, Helvetica, Courier, Courier New, Tahoma, etc. as would beunderstood by the skilled artisan reading the present descriptions. Ofcourse, representative samples of business documents may be scanned atother resolutions, so long as the resulting image resolution is suitablefor recognizing characters on the document. In some approaches, theresolution must be sufficient to provide a minimum character size, suchas a smallest character being no less than 12 pixels in height in oneembodiment. Of course, those having ordinary skill in the art willunderstand that the minimum character height may vary according to thenature of the image. For example different character heights may berequired when processing a grayscale image than when processing a binary(e.g. bitonal) image. In more approaches, characters must besufficiently large to be recognized by optical character recognition(OCR).

In even still more embodiments, method 2300 may include one or more of:estimating one or more dimensions of the digital representation of thedocument based on the estimated resolution of the digital representationof the document; computing an average character width from the averagecharacter dimensions; computing an average character height from theaverage character dimensions; comparing the average character width tothe average character height; estimating an orientation of the digitalrepresentation of the document based on the comparison; and matching thedigital representation of the document to a known document type based onthe estimated dimensions and the estimated orientation.

In an alternative embodiment, estimating resolution may be performed inan inverse manner, namely by processing a digital representation of adocument to determine a content of the document, such as a paymentamount for a digital representation of a check, an addressee for aletter, a pattern of a form, a barcode, etc. as would be understood byone having ordinary skill in the art upon reading the presentdescriptions. Based on the determined content, the digitalrepresentation of the document may be determined to correspond to one ormore known document types, and utilizing information about the knowndocument type(s), the resolution of the digital representation of thedocument may be determined and/or estimated.

Various Embodiments of Mobile Blur Detection

Now with reference to FIG. 24, a method 2400 for detecting one or moreblurred regions in a digital image will be described, according tovarious embodiments. As will be understood and appreciated by theskilled artisan upon reading the present descriptions, method 2400 maybe performed in any suitable environment, such as those discussed hereinand represented in the multitude of figures submitted herewith. Further,method 2400 may be performed in isolation and/or in conjunction with anyother operation of any other method described herein, including but notlimited to image.

In one embodiment, method 2400 includes operation 2402, where, using aprocessor, a tetragon comprising a digital representation of a documentin a digital image is divided into a plurality of sections, each sectioncomprising a plurality of pixels.

In one embodiment, method 2400 includes operation 2404, where, for eachsection it is determined whether the section contains one or more sharppixel-to-pixel transitions in a first direction

In one embodiment, method 2400 includes operation 2406, where, for eachsection a total number of first direction sharp pixel-to-pixeltransitions (S_(S1)) are counted.

In one embodiment, method 2400 includes operation 2408, where, for eachsection it is determined whether the section contains one or moreblurred pixel-to-pixel transitions in the first direction.

In one embodiment, method 2400 includes operation 2410, where, for eachsection a total number of first-direction blurred pixel-to-pixeltransitions (S_(B1)) are counted.

In one embodiment, method 2400 includes operation 2412, where, for eachsection it is determined whether the section contains one or more sharppixel-to-pixel transitions in a second direction.

In one embodiment, method 2400 includes operation 2414, where, for eachsection a total number of second direction sharp pixel-to-pixeltransitions (S_(S2)) are counted.

In one embodiment, method 2400 includes operation 2416, where, for eachsection, it is determined whether the section contains one or moreblurred pixel-to-pixel transitions in the second direction

In one embodiment, method 2400 includes operation 2418, where for eachsection, a total number of second-direction blurred pixel-to-pixeltransitions (S_(B2)) are counted.

In one embodiment, method 2400 includes operation 2420, where for eachsection, it is determined that the section is blank upon determining:S_(S1) is less than a predetermined sharp transition threshold, S_(B1)is less than a predetermined blurred transition threshold, S_(S2) isless than a predetermined sharp transition threshold, and S_(B2) is lessthan a predetermined blurred transition threshold.

In one embodiment, method 2400 includes operation 2422, where for eachnon-blank section, a first direction blur ratio r₁=S_(S1)/S_(B1) isdetermined.

In one embodiment, method 2400 includes operation 2424, where for eachnon-blank section, a second direction blur ratio r₂=S_(S2)/S_(B2) isdetermined.

In one embodiment, method 2400 includes operation 2426, where for eachnon-blank section, it is determined that the non-blank section isblurred in the first direction upon determining that r₁ is less than apredefined section blur ratio threshold.

In one embodiment, method 2400 includes operation 2428, where for eachnon-blank section, it is determined that the non-blank section isblurred in the second direction upon determining that r₂ is less thanthe predefined section blur ratio threshold.

With reference to method 2400, in some approaches a “first direction”and “second direction” may be characterized as perpendicular, e.g. avertical direction and a horizontal direction, or perpendiculardiagonals of a square. In other approaches, the “first direction” and“second direction” may correspond to any path traversing the digitalimage, but preferably each corresponds to a linear path traversing thedigital image. A person having ordinary skill in the art reading thepresent descriptions will appreciate that the scope of the inventiveembodiments disclosed herein should not be limited to only theseexamples, but rather inclusive of any equivalents thereof known in theart.

In one embodiment, method 2400 includes operation 2430, where for eachnon-blank section, it is determined that the non-blank section isblurred upon determining one or more of: the section is blurred in thefirst direction, and the section is blurred in the section direction.

In one embodiment, method 2400 includes operation 2432, where a totalnumber of blurred sections is determined.

In one embodiment, method 2400 includes operation 2434, where an imageblur ratio R defined as: the total number blurred sections divided by atotal number of sections; is calculated.

In one embodiment, method 2400 includes operation 2436, where, it isdetermined that the digital image is blurred upon determining the imageblur ratio is greater than a predetermined image blur threshold.

In various embodiments, method 2400 may include one or more additionaland/or alternative operations, such as described below with continuingreference to FIG. 24. For example, in one embodiment, method 2400 mayalso include determining, for each section a distribution of brightnessvalues of the plurality of pixels; determining a characteristicvariability v of the distribution of brightness values; calculating anoticeable brightness transition threshold η based on v (for example,η=3*v, but not more than a certain value, such as 16); calculating alarge brightness transition threshold μ based on η (for example μ=2*η,but not more than a certain value, such as half of the brightnessrange); analyzing, for each pixel within the plurality of pixels, adirectional pattern of brightness change in a window surrounding thepixel; (for example, horizontally, vertically, diagonally, etc.) andidentifying one or more of: the sharp pixel-to-pixel transition and theblurred pixel-to-pixel transitions based on the analysis.

In another embodiment, method 2400 may also include defining a pluralityof center pixels; sequentially analyzing each of the plurality of centerpixels within one or more small windows of pixels surrounding the centerpixel; such as two pixels before and after; identifying the sharppixel-to-pixel transition upon determining: the large brightnesstransition exists within an immediate vicinity of the center pixel, (forexample, from the immediately preceding pixel to the one following), afirst small (e.g. smaller than noticeable) brightness variation existsbefore the large brightness transition; and a second small brightnessvariation exists after the large brightness transition; detecting thesharp pixel-to-pixel transition upon determining: the large transitionexists within one or more of the small windows, a monotonic change inbrightness exists in the large transition; and detecting the blurredpixel-to-pixel transition upon determining: the noticeable transitionoccurs within a small window; and the monotonic change in brightnessexists in the noticeable transition.

In still another embodiment, method 2400 may also include, for eachsection: counting a total number of sharp transitions in each of one ormore chosen directions; counting a total number of blurred transitionsin each chosen direction; determining that a section is blank upondetermining: the total number of sharp transitions is less than apredefined sharp transition threshold (for example, 50); and the totalnumber of blurred transitions is less than a predefined blurredtransition threshold; determining the non-blank section is blurred upondetermining a section blurriness ratio comprising the total number ofsharp transitions to the total number of blurred transitions is lessthan a section blur ratio threshold (for example, 24%) in at least oneof the chosen directions; and determining that the section is sharp upondetermining the section is neither blank nor blurred.

In yet another embodiment, method 2400 may also include determining atotal number of blank sections within the plurality of sections(N_(blank)); determining a total number of blurred sections within theplurality of sections (N_(blur)); determining a total number of sharpsections within the plurality of sections (N_(sharp)); determining ablurriness ratio (R_(B))=N_(blur)/(N_(blur)+N_(sharp)); and determiningthat the digital image is sharp if the R_(B) is less than a blurrinessthreshold (preferably expressed as a percentage, for example 30%).

Exemplary results of one or more of the foregoing algorithmic processingoperations will now be described with reference to FIGS. 8A-8D. As willbe appreciated by one having ordinary skill in the art, in variousembodiments one or more of the following results may be achievedemploying multiple combinations of operations described herein, invarious sequences. The particular results depicted in FIGS. 8A-8D andthe corresponding descriptions should not be viewed as limiting on thescope of the presently disclosed systems and methods, but rather asexemplary embodiments of one possible process commensurate in scope withthe disclosures set forth herein.

FIG. 8A depicts a digital image 800 comprising a digital representationof a document 802, according to one embodiment.

FIG. 8B depicts a digital image 800 as shown in FIG. 8A after performinga page detection algorithm on the digital image 800, the digital image800 having a detected digital representation of a document 802 therein,according to one embodiment.

FIG. 8C is depicts a digital representation of a document 802 as shownin FIG. 8B, with the background of the digital image 800 having beenremoved and a skew angle of the digital representation of the document802 having been corrected, according to one embodiment.

FIG. 8D is a digital representation of a document 802 as shown in FIG.8C, with the digital representation of the document 802 having beenthresholded to produce a bitonal image.

Various Embodiments of Mobile Capture and Processing Application

Several embodiments of the presently described invention relate toproviding a software application for use in a mobile computingenvironment of a mobile device, the software application comprising aplurality of user interfaces configured to facilitate a user performingone or more actions relating to mobile image capture and processing ofdigital representations of documents and/or associated data. As willbecome apparent from the following descriptions, user interfaces withinthe scope of the present disclosure generally relate to one or more ofmobile image capture, mobile image processing, managing cases with whichmobile images are associated, etc.

FIG. 9 depicts a flowchart 900 of a user interface hierarchy forcapturing and/or processing a digital image comprising a digitalrepresentation of a document, according to one embodiment. Variousoperations depicted in the flowchart 900 may be performed in anyenvironment, including those depicted in FIGS. 1-8 and 10A-19D, invarious approaches. The user interface hierarchy may be particularlyapplicable and/or advantageous to employ in a mobile application such asdepicted particularly in FIGS. 10A-19D. Moreover, operations depicted inthe flowchart 900 may be performed in a multitude of sequences, asdepicted by the arrows in the flowchart 900.

In one embodiment, a user may instantiate a mobile application inoperation 902. Instantiating the mobile application may be performed inany known manner, such as selecting an icon, performing a gesture,tapping a portion of a mobile device display, via an application callfrom another application or another operation within the flowchart 900,etc. as will be understood by one having ordinary skill in the art uponreading the present descriptions.

In particularly secure embodiments, a user instantiating the mobileapplication may be required to provide authentication information to ahost device, such as a server or network switch in operation 904.Authentication may be performed according to any known protocol and byany suitable means understood by skilled artisans upon reading thepresent descriptions, such as via TCP/IP, secure socket layering (SSL),using a virtual private network (VPN) connection, etc.

In more approaches, settings for the user interface and/or algorithmicprocessing to be performed via the user interface may be synchronizedbetween a client device and a host device in operation 906.Synchronization may be performed in any suitable manner and/or by givingpriority to either the host or the client device, i.e. the host maymodify settings for a user account on the host to match settings on theclient device, the client device may modify settings on the client sideto match settings on a user account of the host device, etc. Moreover,synchronization may be account-specific, device-specific, or universal.Synchronization may further include providing and/or resetting settingsto a preconfigured set of default settings, e.g. if a client and/or ahost system crash or malfunction is experienced.

Whether or not authentication was performed in operation 902 and/orsettings were synchronized via operation 904, the user interfacehierarchy may enable users to open one or more new or existing cases inoperation 908. As understood herein, a case may be embodied as adirectory, folder, subfolder, entry in a relational database, or anyother suitable organizational structure suitable for use in a computingenvironment. Moreover, cases may relate to a variety of document-relatedtasks, such as an invoice, an automobile accident report, an insuranceappraisal, patient health care records, a shipping manifesto and/orwaybill, a loan application, etc. as would be understood by one havingordinary skill in the art upon reading the present descriptions.

In one embodiment, opening a case may enable the user to perform one ormore case management operations falling under the general umbrella of acase management operation 910. Exemplary, nonlimiting case managementoperations may include one or more of changing a case type, e.g. inoperation 912, capturing a document image, e.g. in operation 914,reviewing a captured image, e.g. in operation 916, entering case data,e.g. in operation 918, signing a case, e.g. in operation 920, andsubmitting a case, e.g. in operation 922, etc. as would be understood byone having ordinary skill in the art upon reading the presentdescriptions. Of course, case management could include other operationsnot specifically shown in the user hierarchy flowchart 900 as depictedin FIG. 9, and generally include any action relevant to managing a caseas defined herein. Furthermore, each of the general case operations914-922 described above may include one or more sub-operations, as willbe described in greater detail with reference to each specific casemanagement operation below.

With reference to changing case types in operation 912, this operationpermits a user to manipulate a classification associated with aparticular case file, in one embodiment. Case type classification maydetermine other actions capable of and/or scheduled to be performed forthe particular case, and may facilitate efficient handling of cases. Forexample, if a case type is a loan application, other actions to beperformed may include requesting a credit check, acknowledging receiptof a down-payment or escrow payment, approving/denying the loan, etc. aswould be understood by one having ordinary skill in the art. Preferably,the other actions enabled by choosing a particular case type may beespecially relevant to that case type, as in the examples providedabove, and may not include especially irrelevant actions. Withcontinuing reference to the loan application example, such irrelevantactions may include reviewing submitting an invoice, scheduling adoctor's appointment, contacting an emergency authority to respond to anaccident, etc. Other exemplary case types may include accident reports,health care reviews, invoices, shipping actions, etc. as would beunderstood by one having ordinary skill in the art.

With reference to capturing a document image in operation 914, the scopeof the present disclosures includes a variety of embodiments comprisingcapture methods and mechanisms. For example, image capture may includecapturing a digital image using a capture component, e.g. a camera,coupled to or integrated with the mobile device. In such approaches,capture may be performed using photograph capture software native to themobile device, or may utilize an image capture functionality built-in tothe mobile image capture and processing user interface, as will bedescribed in more detail below with reference to FIGS. 15A-16E.

When capturing using the image capture functionality built-in to themobile image capture and processing user interface, a user may selectamong three methods for image capture and subsequent processing.

In one embodiment, via operation 924, a user may capture an image in a“Full Process Mode,” which assists the user in capturing a high qualityimage and subsequently performs a full processing algorithm includingany or all processing functions described herein, as determined by theuser, e.g. using a settings user interface as will be described infurther detail regarding FIGS. 17A-B.

In some approaches, after capturing the image in “Full Process Mode” andprocessing the captured image according to methods described herein, auser may be presented with a Full Process Capture Page Results Output onthe display of the mobile device in operation 926.

In another embodiment, via operation 928, a user may capture an image ina “Preview Mode,” which assists the user in capturing a high qualityimage and subsequently performs a quality-control (QC) analysisincluding processing functions such as detecting the document in theimage, detecting illumination problems, detecting blur, etc. asdescribed herein and as would be understood by one having ordinary skillin the art upon reading the present descriptions.

In various preferred embodiments, and in order to reduce processingrequired to generate a preview in “Preview Mode,” image processingoperations may be performed in a particularly efficient order. Forexample, in one approach illumination problems and/or blurred regionsmay be detected immediately after detecting a digital representation ofa document in a digital image. Similarly, in more approaches resolutionestimation and matching to known document types may be performed afterpage detection, but before rectangularizing, deskewing and/or croppingthe digital representation of the document in the digital image.

In some approaches, after capturing the image in “Preview Mode” andprocessing the captured image according to methods described herein, auser may be presented via an image capture QC results user interface onthe display of the mobile device in operation 930. The “Preview Mode”interface and the an image capture QC results user interface will bedescribed in further detail below with reference to FIG. 15C.

In some approaches, capturing an image in a “Full Process Mode” and a“Preview Mode” may utilize a substantially identical image capture userinterface, which will be described in further detail below with regardto FIGS. 15A-15B.

In still another embodiment, via operation 932, a user may capture animage in a “Mobile Scanner” mode, which captures image data from amobile scanner in communication with the mobile device. “Mobile ScannerMode” may include processing as described above for “Full Process Mode”or “Preview Mode,” as may follow default settings or as may bedetermined by the user. The mobile scanner image capture user interfacewill be described in more detail below with reference to FIGS. 16D-16E.

In some approaches, after capturing the image in “Mobile Scanner Mode”and processing the captured image according to the methods describedherein, a user may be presented with a mobile scanner image captureresults user interface on the display of the mobile device in operation934. The mobile scanner image capture results user interface may besubstantially similar to the image capture and processing results userinterface, the image capture QC results user interface, or combinationsthereof, as will be understood by one having ordinary skill in the artupon reading the present descriptions.

When capturing using the interface native to the mobile device, usersmay capture the image according to the methodology set forth in suchinterface. Subsequently, in operation 936, a user may designate thecaptured image for processing, using a capture image attachment userinterface such as described below regarding FIG. 16A. Alternatively, thecapture attachment user interface depicted in FIG. 16A may be utilizedto capture images in “Full Process Mode” and/or “Preview Mode, in someapproaches.

In some approaches, after capturing the image in “Capture AttachmentMode,” a user may be presented with a capture attachment results userinterface on the display of the mobile device in operation 938. Thecapture attachment results user interface may be substantially similarto the image capture and processing results user interface, the captureimage QC user interface, or combinations thereof, as will be understoodby one having ordinary skill in the art upon reading the presentdescriptions.

In particularly preferred embodiments, users may additionally and/oralternatively capture data other than image data. For example, users maycapture image data as described above, video data, and/or audio datarelating to a case. As a nonlimiting example, a user may capture a videoshowing perspectives of a walk-around detailing damage to an automobileafter an accident, or depicting the condition of real property forpurposes of insurance, appraisal, etc. as would be understood by onehaving ordinary skill in the art upon reading the present descriptions.Similarly, audio data relating to a case may include a recordedstatement, for example by an individual involved in an accident, awitness, a police officer responding to the accident, etc. as would beunderstood by one having ordinary skill in the art upon reading thepresent descriptions. Exemplary embodiments of the capture videoattachment user interface and capture audio attachment user interfacewill be described below with reference to FIGS. 16B-16C.

As shown in FIG. 9, image data to be associated with a case isrepresented by element 940, video data by element 942, and audio data byelement 944, all of which may be included and/or associated with any ofthe capture modes and/or results outputs described above and depicted inFIG. 9.

Now, with reference to reviewing captured image(s) in operation 916, invarious approaches a user may review a captured image, whether capturedin “Full Process Mode,” “Preview Mode,” “Mobile Scanner Mode” or“Capture Attachment Mode” as described above. Operation 916 may includefunctionalities such as editing the captured image in operation 946,deleting the captured image in operation 948, and/or enhancing thecaptured image in operation 950.

As understood herein, editing the captured image in operation 946 mayinclude manually cropping, zooming, rotating, etc. the image. Editingmay also include manually adjusting the image aspect ratio, brightness,contrast, sharpness, tint, color, etc. according to any known method invarious approaches. Editing may further include re-capturing the image,in various approaches. In still more approaches, editing may includedesignating a document type, size, or format, which may include anyknown type, size or format such as U.S. letter, A3, A4, A5, ledger,legal, check, business card, etc. as would be understood by one havingordinary skill in the art upon reading the present descriptions. Inpreferred approaches, a user may edit the image using an edit objectaction interface such as will be described below with reference to FIG.13E. The edit object action interface may facilitate a user editing theimage by providing access to further interfaces, such as a crop objectuser interface and/or a constrain object user interface as discussedbelow with reference to FIGS. 13F-13G.

Deleting the image in operation 948 may include removing any associationbetween the image and the case, removing any association between theimage and the mobile image capture and processing application, deletingthe image from a memory of the mobile device, overwriting a portion ofthe memory of the mobile device storing data corresponding to the image,etc. as would be understood by one having ordinary skill in the art uponreading the present descriptions. Case objects may be deleted in oneembodiment using a delete object user interface such as described belowwith reference to FIG. 13C.

Enhancing the image in operation 950 may include processing the imageusing one or more algorithmic functionalities described above, includingdetecting and/or correcting illumination problems, detecting and/orcorrecting blur, detecting and/or correcting skew, rendering the imagebitonal, etc. as would be understood by one having ordinary skill in theart upon reading the present descriptions.

With reference to entering case data in operation 918, a user may enterinformation relating to a case and/or images captured and associatedwith a case. Any relevant information may be entered. For example, if acase type is a loan application, a user may enter information such as aloan number, a customer name, a date, a location, an account number, acustomer address, a guarantor name, a loan amount, etc. as would beunderstood by one having ordinary skill in the art upon reading thepresent descriptions.

Moreover, case data may include alphanumeric characters, itemsselectable from a drop-down list or field, predetermined optionsselectable via check boxes, toggle switches, metadata, metadata labels,etc. as would be understood by one having ordinary skill in the art uponreading the present descriptions. In preferred embodiments, users mayenter case data via an enter case data interface. One exemplaryembodiment of an enter case data user interface will be described infurther detail below with reference to FIG. 13I.

With reference to signing cases in operation 920, a user may capture asignature relating to a case. Signatures may be captured using any knownmethod, and may include any type of signature typically associated witha business process, for example a handwritten signature, an electronicsignature, a digital signature, etc. as would be understood by onehaving ordinary skill in the art upon reading the present descriptions.In preferred embodiments, a user may capture a signature using a capturesignature user interface, such as will be described according to oneembodiment with reference to FIG. 13J, below. Of course other methods ofcapturing signatures are within the scope of the present disclosures.

With reference to submitting cases in operation 922, a user may review,delete, and or submit a case, e.g. to a central server or other host.Case submission may be advantageous because a user may subsequentlydelete the case from the mobile device, freeing memory for other sues,may request a peer-review process be performed for the submitted case,may forward or escalate a case to facilitate further processing thereof,etc. as would be understood by one having ordinary skill in the art uponreading the present descriptions. Moreover, case submission allows suchactions to be taken remotely, obviating the need to physically travel toa submission location, as well as reducing the amount of resources andtime needed to complete processing of a particular case. In preferredembodiments, a user may submit a case by using a submit case userinterface such as described in further detail below with reference toFIG. 13K.

User Interfaces for Mobile Image Capture and Processing

The following descriptions will set forth exemplary, nonlimitingembodiments of user interfaces suitable for performing one or more ofthe functionalities described above with particular reference to FIG. 9.Of course, additional, alternative, and/or equivalent interfaces may beemployed in other embodiments without departing from the scope of thepresent disclosures.

The user interfaces described below may be employed in any environment,and may be used to facilitate performing any functionality describedherein, including those described above with reference to FIGS. 1-9, invarious approaches.

In one embodiment, a method 2500 for providing a mobile image captureand processing user interface may include a plurality of operations suchas described below. Furthermore, method 2500 may be performed in anysuitable environment, including those described herein and/orrepresented in the various Figures presented herewith.

Moreover, as will be appreciated by one having ordinary skill in the artupon reading the present descriptions, method 2500 may includeperforming any combination of image processing operations as describedwith reference to the myriad Figures submitted herewith, in variousapproaches. Of course other functionalities equivalent to thosedescribed herein may also be facilitated or performed by using one ormore aspects of the user interfaces presently disclosed, as would beappreciated by one having ordinary skill in the art upon reading thepresent descriptions.

Referring now to FIGS. 25-27, two methods for providing user interfacefunctionalities will be described according to several illustrativeembodiments. Upon reading the present disclosures, the skilled artisanwill appreciate that the scope of the inventive embodiments representedherein are not limited to the methods presented in FIGS. 25-27.

In one embodiment, particularly as shown in FIG. 25, a method 2500 forproviding a mobile image capture and processing user interface mayinclude a plurality of operations such as described herein. Furthermore,method 2500 may be performed in any suitable environment, includingthose described herein and/or represented in the various Figurespresented herewith.

Moreover, as will be appreciated by one having ordinary skill in the artupon reading the present descriptions, method 2500 may be performed inany suitable environment, such as any described herein and/orrepresented in the various Figures presented herewith, among others,without departing from the scope of the present disclosures, accordingto various embodiments.

In one embodiment, and particularly as shown in FIG. 25, method 2500includes operation 2502, where a request to capture data is received. Asunderstood herein, capture data may include one or more of a digitalimage and a digital video.

In another embodiment, method 2500 includes operation 2504, where datais captured using a capture component of a mobile device in response toreceiving the capture request. Notably, in some approaches one or moreimage processing operations may be performed upon capturing the data,such as page detection, illumination correction, or other functionsdescribed herein. Thus, the captured data may be the raw image or video,a processed version of the raw image or video, some portion of theinitial or processed image or video such as an image of a detecteddocument extracted therefrom, etc. as would be appreciated by one havingordinary skill in the art upon reading the present descriptions.

In yet another embodiment, method 2500 includes operation 2506, wherethe captured data is output to one or more of a display of the mobiledevice, a processor of the mobile device, and a storage medium of themobile device; e.g. to a mobile device display, a mobile deviceprocessor and/or memory; a server processor and/or memory, etc. as wouldbe appreciated by one having ordinary skill in the art upon reading thepresent descriptions.

In still yet another embodiment, method 2500 includes operation 2508,where a request to analyze the captured data is received, e.g. via themobile device.

In one approach, method 2500 includes operation 2510, where, in responseto receiving the request to analyze the captured data, the captured datais analyzed using the mobile device. For example, analyzing the captureddata using the mobile device may include processing the captured datausing a mobile device processor according to one or more instructionscorresponding to, for example, image processing operations as discussedherein.

In still more embodiments of method 2500, the captured data maycorrespond to a digital representation of a document, e.g., a digitalimage of a document, and analyzing the data such as in operation 2510may include comparing one or more characteristics of the digitalrepresentation of the document to one or more quality control (QC)metrics (e.g. comparing a characteristic value to a QC threshold);determining whether each characteristic is acceptable based on thecomparison and, for each characteristic: outputting an indication thatthe characteristic is acceptable upon determining the characteristic isacceptable, outputting an indication that the characteristic is notacceptable upon determining the characteristic is not acceptable, andoutputting an indication that the digital representation of the documentis acceptable upon determining that each characteristic is acceptable.

Moreover, the one or more quality control metrics may include one ormore of: a page detection metric (e.g. whether a page detectionoperation was successful and/or produced a sufficiently reliable resultsuch as indicated by one or more checks described above with referenceto FIG. 19, an illumination metric, e.g. whether one or moreillumination problems exist such as described above with reference toFIG. 21-22; and a blur metric, e.g. whether one or more blurred regionsexist in the digital image, such as described above with reference toFIG. 25.

In some approaches, method 2500 may further include displaying, via adisplay of the mobile device, the indication that the characteristic isnot acceptable; receiving instructions to recapture data in response tothe displaying. Notably, as understood herein “recapture data” does notmean to capture the same data as originally captured, but rather tocapture data again, the data corresponding to the digital representationof the document, for example taking a new picture, video, etc of thetarget document to attempt processing using the new picture, video, etc.Method 2500 may thus include recapturing the data in response toreceiving the instructions; and outputting the recaptured data, invarious embodiments.

Additionally and/or alternatively, after displaying the indication thatthe characteristic is not acceptable, method 2500 may proceed byreceiving instructions to enhance the captured data in response to thedisplaying; enhancing the captured data in response to receiving theinstructions without recapturing the data; e.g. correcting blur,illumination, skew, performing page detection with different settings(smaller step, different path, modified threshold values, etc. asconsistent with the descriptions provided herein and would be understoodby a skilled artisan reviewing these disclosures) and outputting theenhanced data.

In one implementation, method 2500 includes operation 2512, where aresult of the analyzing is output to one or more of the display of themobile device, the processor of the mobile device, and the storagemedium of the mobile device.

In various embodiments, method 2500 includes may include additionaland/or alternative operations for providing an image processingapplication functionality.

For example, in one embodiment method 2500 may additionally and/oralternatively include: receiving a request to modify one or more capturedata parameters; and modifying the one or more capture data parametersin response to the request. Moreover, the one or more capture dataparameters may include one or more of: a flash setting (e.g. on, off,auto); a capture stability assistance setting; e.g. outputtinginstructions to hold camera over document, warning that camera ismoving, instructions to hold camera still, etc. a capture alignmentassistance setting; e.g. gridlines ON/OFF, number of gridlines in eachdirection (horizontal, vertical), grid dimensions, etc. a zoom level; acapture color mode; e.g. black/white, color, RGB, CMYK, greyscale, etc.a capture data destination; e.g. processor, memory, etc. as would beappreciated by one having ordinary skill in the art upon reading thepresent descriptions.

In still yet more embodiments, the captured data may correspond to adigital representation of a document; and method 2500 may additionallyand/or alternatively include one or more of: outputting the digitalrepresentation of the document to a display of the mobile device; andreceiving user input corresponding to instructions to modify the digitalrepresentation of the document. For example, user input corresponding toinstructions to modify the digital representation of the document mayinclude manual manipulation by the user—crop, rotate, zoom, brightness,contrast, sharpness, color, tint, document boundaries, etc. as would beunderstood by one having ordinary skill in the art upon reading thepresent descriptions. Moreover, user input corresponding to instructionsto modify the digital representation of the document may includeinstructions to perform one or more automated operations to modify thedigital representation of the document, such as any of the imageprocessing operations described herein.

In additional and/or alternative approaches, method 2500 may alsoinclude: receiving metadata corresponding to the captured data; andassociating the metadata with the captured data. As will be appreciatedby one having ordinary skill in the art, metadata may include any typeof metadata known in the art, and may be associated with the captureddata by any suitable means. In one particular approach the metadatacorrespond to one or more of: alphanumeric characters, symbols,signatures, e.g. handwritten, electronic, digital, etc.; and pointers,e.g. file pointers, hash/array references, etc.

In more approaches to method 2500, capturing the data may includereading the data from a storage medium of the mobile device, for examplereading an attachment.

In still yet more approaches to method 2500, the captured data maycorrespond to a digital representation of a document, and method 2500may further include: correcting one or more artifacts in the captureddata by rectangularizing the digital representation of the document. Asunderstood herein, artifacts include any characteristic that may beimparted on an image by virtue of being captured using a camera asopposed to a flat-bed scanner, such as a distortion of one or moreportions of the digital representation of the document (e.g. documentedge appears curved in image but is truly straight, fishbowl-typeeffects, projective effects arising from capture perspective, etc.), anda skew angle of the digital representation of the document.

Referring now to FIG. 26, a method 2600 for providing a case managementuser interface is shown, according to one embodiment. As will beappreciated by one having ordinary skill in the art upon reading thepresent descriptions, method 2600 may be performed in any suitableenvironment, such as any described herein and/or represented in thevarious Figures presented herewith. Moreover, case management userinterface provided according to method 2600 may be used in conjunctionwith any of the image processing operations and/or interfaces describedherein without departing from the scope of the present disclosures.

In one embodiment, method 2600 includes operation 2602, where a casemanagement interface is outputted to a display of a mobile device.

In one embodiment, method 2600 includes operation 2604, where one ormore instructions corresponding to one or more case actions are receivedvia the displayed case management interface, each case action beingassociated with at least one case. In various approaches, case actionsmay include one or more of creating a new case; opening an existingcase; deleting one or more of the existing cases; designating a casetype; capturing case information; capturing data corresponding to adigital representation of a document the data comprising either imagedata or video data; outputting the captured data to the display of themobile device; associating the captured data with one or more of the newcase and the existing case; dissociating the captured data from one ormore of the new case and the existing case; processing the captureddata; outputting the processed data to the display of the mobile device;receiving user input via the display of the mobile device; andsubmitting one or more of the new case and the existing case to a remoteserver. printing one or more documents related to one or more of the newcase and the existing case; associating the case information with one ormore of the new case and the existing case; capturing a signature;detecting the digital representation of the document; and associatingthe signature with one or more of the new case and the existing case.

Moreover, printing one or more documents related to one or more casesmay include submitting a print request from the mobile device to aremote resource, the remote resource and the mobile device not being incommunication via a local network, i.e. connected to two separate LANs,WANs, WLANs, separately connected to a cell network and a local network,etc.; and printing the one or more documents at the remote locationusing the remote resource in response to the print request.

In one embodiment, method 2600 includes operation 2606, where the one ormore case actions are performed in response to receiving theinstructions.

In various embodiments, method 2600 may include one or more additionaland/or alternative operations, such as described below with continuingreference to FIG. 26. For example, in one embodiment, method 2600 mayalso include outputting a data capture interface to the display of themobile device for capturing an image comprising a digital representationof a document; receiving a request from a user to capture the image viathe data capture interface; capturing the image in response to receivingthe request, the capturing being performed using a capture component ofthe mobile device; and associating the captured image with one or morecases.

In some approaches commensurate with method 2600, capturing may includeone or more of receiving user input via the display of the mobiledevice; capturing data using a capture component of the mobile device inresponse to the user input and reading the data from a computer-readablestorage medium of the mobile device in response to the user input.

In more approaches, user input may correspond to metadata related to thecase, such as alphanumeric characters, symbols, signatures; e.g.handwritten, electronic, digital, etc. and pointers. e.g. file pointers,hash/array references, etc. as would be understood by one havingordinary skill in the art upon reading the present descriptions. In suchapproaches, method 2600 may further include associating the metadatawith one or more of the new case and the existing case.

Various approaches to implementing method 2600 may further and/oroptionally include displaying a plurality of potential case types on thedisplay of the user device; receiving user input via the display of themobile device, the user input indicating one of the plurality of thepotential case types is a desired case type; and designating the casetype as the desired case type. Moreover, the potential case types may beinclusive of: an insurance claim; a loan application; a proof ofdelivery; an undefined case type; a new account opening; an educationalprogram application; a medical record; an expense management; anaccident report; and a user-defined case type.

Of course, similar to the principles set forth above with reference toFIG. 26 and method 2600, method 2600 may also be utilized in anycombination with any other methodology and in any environment describedherein, as well as equivalents thereof that would be appreciated by onehaving ordinary skill in the art upon reading the present descriptions.

For example, in one embodiment method 2600 may be utilized inconjunction with any of the image processing operations discussed withreference to FIGS. 1-9 and 19-25, above. Exemplary operations suitablefor use with method 2600 and/or the corresponding case managementinterface provided thereby include page detection, pagerectangularization, illumination problem detection and/or correction,resolution estimation, blur detection, etc. as would be understood byone having ordinary skill in the art upon reading the presentdescriptions.

In yet another embodiment, method 2600 may additionally and/oralternatively include outputting an authentication interface to adisplay of the mobile device; receiving authentication data via theauthentication interface; receiving an authentication request;determining whether the authentication data is valid in response toreceiving the authentication request; granting access to one or moresecure case actions, where any case action could be a secure case actionin a particular context, upon determining the authentication data isvalid; and denying access to the secure case actions upon determiningthe authentication data is not valid.

In another embodiment, method 2600 may also include: correcting one ormore artifacts in the image by rectangularizing the digitalrepresentation of the document.

As described herein, artifacts may include any characteristic that maybe imparted on an image by virtue of being captured using a camera asopposed to a flat-bed scanner. For example, artifacts may include one ormore of: a distortion of one or more portions of the digitalrepresentation of the document, (e.g. document edge appears curved inimage but is truly straight, fishbowl-type effects, projective effectsarising from capture perspective, etc.) and a skew angle of the digitalrepresentation of the document. Of course other artifacts as would beknown to skilled artisans upon reading the present descriptions are alsowithin the scope of these disclosures.

Users may interact with the various components of each user interfaceusing any known methods of interaction, including tapping one or moreregions on a mobile device display, performing one or more gestures(e.g. swiping, pinching, spreading, scrolling, panning, etc.), eachusing one or more points of contact, etc. as would be understood by onehaving ordinary skill in the art upon reading the present descriptions.

Referring now to FIGS. 10A-18B, various schematics of mobile applicationuser interfaces commensurate in scope with present disclosures will bedescribed according to several illustrative embodiments. The scope ofthe inventive embodiments represented herein are not limited to thefeatures and embodiments presented in FIGS. 10A-18B, but rather includeall equivalents thereof as would be known to one having ordinary skillin the art upon reading the present disclosures.

Furthermore, a skilled artisan reading the instant descriptions willappreciate that various embodiments of the user interfaces depicted inFIGS. 10A-18B may be utilized to facilitate and/or perform any operationof the algorithmic processes described herein, including thoserepresented in FIGS. 1-9 and 26-26, among others.

With particular reference to FIG. 10A, a schematic representation of auser authentication interface 1000 of an application for capturingand/or processing a digital image comprising a digital representation ofa document is shown, according to one embodiment.

In some approaches, a user authentication interface 1000 may include aplurality of objects, including one or more data entry fields 1002,interactive buttons and/or and toggle switches 1004, 1006. Preferably,the user authentication interface also includes a title bar 1008describing the presently displayed interface to the user.

As will be appreciated by one having ordinary skill in the art, uponinteracting with a data entry field 1002, e.g. by tapping a region ofthe mobile device displaying the data entry field 1002, a user may enterdata into the data entry field 1002 using any known method. For example,upon interacting with a data entry field 1002, the user may be presentedwith an input interface such as a QWERTY-layout keyboard represented onthe display of the mobile device. Additionally and/or alternatively auser may select one of a predetermined set of data entries from adrop-down list, which may or may not be created and/or supplemented fromdata previously entered by the user and/or default entries, in oneembodiment. In particularly preferred embodiments, a user may enter oneor more of a user ID and a password in the data entry fields 1002 of theuser authentication interface 1000.

As will also be understood by one having ordinary skill in the artreading the present descriptions, in more embodiments the user mayinteract with interactive buttons 1004, 1006 using any knownmethodology, e.g. tapping, swiping, gesturing, etc. With particularreference to user authentication interface 1000, upon interacting withan interactive button 1004, 1006, the mobile application may take anaction, such as saving a user ID, attempting to log a user in to a useraccount using the authentication information provided in data fields1002, provide information about the mobile application, and/or cancel anauthentication attempt.

FIG. 10B is a schematic representation of a host connection userinterface 1010 of an application for capturing and/or processing adigital image comprising a digital representation of a document,according to one embodiment. As shown in FIG. 10B, host connection userinterface includes a plurality of data entry fields 1014 and interactivebuttons 1012. Data entry fields may operate in a manner substantiallysimilar to that described above regarding FIG. 10A, and in the hostconnection user interface 1010 may preferably relate to one or more of auser ID, a password, and one or more host device URLs. Similarly,interactive buttons 1012 may operate in a manner substantially similarto that described above regarding FIG. 10A, and in the host connectionuser interface 1010 may preferably assist a user in navigating back tothe user authentication interface 1000 and/or a settings interface asdescribed in detail below with reference to FIGS. 17A-B. Optionally, oneor more of the interactive buttons 1012 may initiate an authenticationattempt using the authentication information provided in data fields1014.

FIG. 11 is a schematic representation of a case creation user interface1100 of an application for capturing and/or processing a digital imagecomprising a digital representation of a document, according to oneembodiment. As shown in FIG. 11, case creation user interface 1100includes a title region 1106 that may display indicia that the user isinteracting with a case creation user interface, and may additionallyand/or alternatively display information identifying an authenticateduser account associated with one or more cases. For example, userauthentication information may include a username entered in anauthentication interface 1000 or 1010, as discussed above regardingFIGS. 10A and/or 10B, in some embodiments.

In various approaches, case creation user interface 1100 may furtherinclude a plurality of interactive buttons 1102 for facilitating one ormore case creation actions by the user. For example, case creationactions may include one or more of opening a new case, opening a pendingcase, and retrieving information about a selected case. Moreover, casecreation user interface 1100 may include a plurality of fields 1104arranged in a list format, each field displaying information relating toone or more existing cases and displaying relevant case information toassist a user in identifying the case. For example, fields 1104 maydisplay information such as a case type, a case number, a case openingdate, a name of an individual associated with the case, etc. as would beunderstood by one having ordinary skill in the art upon reading thepresent descriptions. In preferred embodiments, cases having informationdisplayed in one of the plurality of fields 1104 may be characterized as“pending” cases, i.e. cases that were previously opened but not yetsubmitted to a host device.

In one embodiment, upon a user interacting with an interactive button1102 designed to facilitate opening a new case, the user may bepresented with a list of case types from which the user is prompted toselect an appropriate case type to associate with the newly opened case.Upon selecting a case type, the new case may have associated therewithone or more case-type-specific properties, such as a document page size,a number of pages, a color mode (e.g. black and white, greyscale, color,auto, etc.) and/or be provided an interface for associating one or moreattachments (e.g. electronic documents) with the case.

Moreover, upon opening a new case and/or an existing case by interactingwith one or more of the interactive buttons 1102 and/or fields 1104, themobile application displaying the case creation interface may optionallysynchronize case data with information stored on a host, e.g. a centralserver. However, if the mobile device is not in connection with thehost, or is unable to establish or authenticate a secure session withthe host, the mobile application may utilize locally cached datarelating to the case. Synchronization and local data caching may beperformed using any known methodology, in various approaches.

Upon opening a new case or selecting an existing case, in severalembodiments a user may be directed to a case object management userinterface such as case object management user interface 1200substantially as shown in FIG. 12. In one embodiment, case objectmanagement user interface 1200 may display one or more case objects1202-1206, which may or may not be associated with the case. Inembodiments where case objects 1202-1206 are not associated with thecase, the case objects 1202-1206 may be files stored on a storage mediumintegrated into or coupled to the mobile device, e.g. a storage mediumof the mobile device, the host device, or a storage medium otherwisecoupled to the mobile device. Further, in embodiments where one or morecase objects 1202-1206 are not associated with the case, case objectmanagement user interface may facilitate a user associating one or moreof the case objects 1202-1206 with the open case. In furtherembodiments, case object management user interface 1200 may facilitate auser associating data stored on a storage medium of the mobile device,the host, etc. but not displayed in the case object management userinterface by providing an interactive button 1208 configured to assistthe user in navigating to a display (not shown) depicting the datastored on the storage medium. As will be appreciated by one havingordinary skill in the art reading the present descriptions, such adisplay may be native to the mobile device operating system, the hostoperating system, etc.

As understood herein, case objects may include one or more of digitalimage(s), digital video(s), audio, etc. as would be understood by onehaving ordinary skill in the art upon reading the present descriptions.Moreover, in one embodiment case object management user interface 1200may further include a summary 1210 displaying a number of case objects1202-1206 and/or case object types associated with the case. Forexample, if a case has associated therewith five images 1202, threevideos 1204, and two audio clips 1206 (as shown in FIG. 12), thensummary 1210 may display information communicating these data to theuser, e.g. “5 images, 3 videos, 1 audio.”

In some approaches, and similar to the interfaces discussed above, anexemplary case object management user interface 1200 may include a titleregion 1212 that may display indicia that the user is interacting with acase object management user interface, and may additionally and/oralternatively display information relating to the case with which theone or more case objects 1202-1206 are associated.

Users may preferably interact with one or more case objects 1202-1206 inorder to facilitate performing one or more case management actionsrelating to or on the case objects 1202-1206. For example, a user mayselect one or more case objects 1202-1206 by tapping on a region of themobile device display rendering the case object(s) 1202-1206. Uponselecting one or more case objects 1202-1206, the renderedrepresentation of the selected case object(s) 1202-1206 may be modifiedto provide indicia that the case object(s) 1202-1206 have been selected,e.g. by overlaying a display such as a mark 1302 as depicted in theselected object management user interface 1300 shown in FIG. 13A,according to one embodiment.

In another embodiment, upon selecting one or more case object(s)1202-1206, case object management user interface 1200 may displayadditional information not described above regarding FIG. 12, such asdisplaying the mark(s) 1302 and/or displaying a total number of selectedcase objects 1202-1206, e.g. in a title region 1304 as depicted in FIG.13A.

Moreover, selected object management user interface may display one ormore interactive buttons 1306 which facilitate performing one or morecase actions on selected case objects 1202-1206. For example,interactive buttons 1306 may facilitate performing a processingalgorithm on one or more selected case objects 1202-1206. In preferredembodiments, the processing algorithm may include one or more functionssubstantially as described above, and particularly as described aboveregarding FIGS. 3-9. Processing may be performed using a processor ofthe mobile device in a background process, a foreground process, etc.,or may alternatively be performed using a processor of a host device.

In additional approaches, case actions may further include deleting theone or more selected case objects 1202-1206, for example by selectingthe one or more case objects 1202-1206 and subsequently interacting witha delete button, which may be one of the interactive buttons 1306. Caseactions may further include any of the operations discussed herein andparticularly with reference to FIGS. 3-9, and in various embodiments theselected case object management interface 1300 may facilitate performingone or more of such case actions upon a user double-tapping a caseobject 1202-1206, whereupon the selected case object managementinterface 1300 may direct a user to a list of case actions capable ofbeing performed in relation to the double-tapped case object 1202-1206.

In an exemplary approach, a user may be assisted in performing caseactions upon double-tapping case object 1202-1206 via a case managementaction user interface 1310 such as shown in FIG. 13B. In the embodimentdepicted in FIG. 13B, case management action user interface 1310includes a plurality of interactive buttons 1312 configured tofacilitate performing one or more case actions on a selected case object1202-1206 or generally for the case currently open. In addition, casemanagement action user interface 1310 may include a title region 1314configured to display indicia that the user is currently interactingwith case management action user interface 1310, and/or informationrelating to the case currently open.

As described above, case actions may include any operation discussedherein, and may preferably include one or more operations discussedabove with reference to FIGS. 3-9, such as capturing an image, a video,audio, etc. relating to the case, entering information relating to thecase, signing the case, submitting the case, modifying the case type,deleting the case, etc. Moreover, in particularly preferred embodimentsinteracting with one of the interactive buttons 1312 may direct a userto a corresponding user interface such as described below.

FIG. 13C is a schematic representation of a delete object user interface1320 of an application for capturing and/or processing a digital imagecomprising a digital representation of a document, according to oneembodiment. In embodiments where one of the interactive buttons 1312displayed in case management action user interface 1310 facilitatesperforming a delete object case action, upon a user interacting with theinteractive button 1312 corresponding to the delete object case action,a user may be directed to a delete object user interface 1320 such asdisplayed in FIG. 13C.

In one embodiment, delete object user interface 1320 may include aplurality of interactive buttons 1322 and a title region 1324.Interactive buttons 1322 may facilitate one or more case actions such asdeleting a case object 1202-1206 associated with a case, deleting anentire case, and/or navigating between delete object user interface 1320and any other user interface described herein. Title region 1324 maydisplay indicia that a user is being presented a delete object userinterface 1320, information about the case, etc. as would be understoodby one having ordinary skill in the art upon reading the presentdescriptions.

FIG. 13D is a schematic representation of an edit object user interface1330 of an application for capturing and/or processing a digital imagecomprising a digital representation of a document, according to oneembodiment. In embodiments where one of the interactive buttons 1312displayed in case management action user interface 1310 facilitatesperforming an edit object case action, upon a user interacting with theinteractive button 1312 corresponding to the edit object case action, auser may be directed to an edit object user interface 1330 such asdisplayed in FIG. 13D.

In one embodiment, edit object user interface 1330 may include aplurality of interactive buttons 1332 and an action button 1334, as wella title region 1336. Interactive buttons 1332 may facilitate one or morecase actions such as saving a case object 1202-1206 associated with acase, undoing one or more edits performed on the case object 1202-1206and/or navigating between edit object user interface 1330 and any otheruser interface described herein. Action button 1334 may particularlyfacilitate performing one or more edit actions on the case object1202-1206 by enabling a user to interact with the case object 1202-1206using one or more edit object action tools, such as a crop tool, aconstrain tool, etc. such as described herein with reference to FIGS.13E and 13F, and as would be understood by one having ordinary skill inthe art upon reading the present descriptions.

Moreover, title region 1336 may display indicia that a user is beingpresented an edit object user interface 1330, information about thecase, etc. as would be understood by one having ordinary skill in theart upon reading the present descriptions.

FIG. 13E is a schematic representation of an edit object action userinterface 1340 of an application for capturing and/or processing adigital image comprising a digital representation of a document,according to one embodiment. In the exemplary embodiment depicted inFIG. 13E, edit object action user interface 1340 includes a plurality ofinteractive buttons 1342 and a title region 1344. Title region 1344 maydisplay indicia that a user is being presented an edit object actionuser interface 1340, information about the case, etc. as would beunderstood by one having ordinary skill in the art upon reading thepresent descriptions.

In several approaches, interactive buttons 1342 may facilitate one ormore case actions such as saving a case object 1202-1206 associated witha case, undoing one or more edits performed on the case object 1202-1206and/or navigating between edit object user interface 1340 and any otheruser interface described herein. Interactive buttons 1342 mayparticularly facilitate performing one or more edit actions on the caseobject 1202-1206 by enabling a user to manually interact with the caseobject 1202-1206 using one or more edit object action tools, such as acrop tool, a constrain tool, a brightness tool, a sharpness tool, acontrast tool, a tint tool, etc.

In additional embodiments, interactive buttons 1342 may facilitateperforming one or more algorithmic operations as described herein on acase object 1202-1206. For example, a user may interact with one or moreinteractive buttons 1342 to initiate an illumination enhancementprocess, a blur detection process, a page detection process, a pagetransformation (e.g. rectangularization) process, a deskew process, arecapture process, etc. as would be understood by one having ordinaryskill in the art upon reading the present descriptions.

FIG. 13F is a schematic representation of a crop object user interface1350 of an application for capturing and/or processing a digital imagecomprising a digital representation of a document, according to oneembodiment. As shown in FIG. 13F, crop object user interface 1350includes a title region 1359, a plurality of interactive buttons 1352, adigital representation of a document 1354, a crop tool comprising awindow characterized by one or more edges 1356 and a plurality ofcorners 1358. At any point, and preferably by interacting with one ormore of the interactive buttons 1352, a user may cancel the cropoperation and navigate to another of the user interfaces describedherein, may save the results of the crop operation, or may reset thewindow to a predetermined default location. In some approaches, one ofthe interactive buttons 1352 may facilitate a user interacting with aconstrain tool such as will be described below with reference to FIG.13G.

Title region 1359 may display indicia that a user is being presented anedit object action user interface 1350, information about the case, etc.as would be understood by one having ordinary skill in the art uponreading the present descriptions.

In preferred embodiments, a user may interact with one or more of theedges 1356 and/or corners 1358 in order to adjust boundaries of thewindow as desired, e.g. to include only the digital representation ofthe document 1354 and a minimal portion of the background of the imagecomprising the digital representation of the document 1354.

FIG. 13G is a schematic representation of a constrain object userinterface 1360 of an application for capturing and/or processing adigital image comprising a digital representation of a document,according to one embodiment. As shown in FIG. 13G, constrain object userinterface 1360 includes a title region 1364, a plurality of interactivebuttons 1362, and digital representation of a document 1354. In someapproaches, a user may be presented with constrain object user interface1360 upon interacting with one of the interactive buttons 1352 of cropobject user interface 1350, said button being configured to facilitatethe user interacting with the constrain object user interface 1360.

In one embodiment, each of the plurality of interactive buttons 1362 maycorrespond to a known document size, such as an 8.5″×11″ letter, an8.5″×14″ legal document, an A3-size document, an A4-size document, anA5-size document, a ledger, a business card, a personal check, etc. aswould be understood by one having ordinary skill in the art upon readingthe present descriptions. Of course, interactive buttons 1362 may alsocorrespond to other known document sizes, as would be appreciated by onehaving ordinary skill in the art upon reading the present descriptions.Further still, interactive buttons 1362 may also facilitate a usernavigating between constrain object user interface 1360 and any otheruser interface described herein, as well as cancel/undo a constrainoperation such as described below.

In preferred embodiments, upon a user interacting with one of theinteractive buttons 1362, a size and aspect ratio of the window(comprising edges 1356 and corners 1358) may be set to a known aspectratio and size corresponding to the type of document represented by theinteractive button 1362 with which the user interacted. The user maythen adjust the location of the window to encompass the digitalrepresentation of the document 1354, in one approach.

Title region 1364 may display indicia that a user is being presented anedit object action user interface 1360, information about the case, etc.as would be understood by one having ordinary skill in the art uponreading the present descriptions.

FIG. 13H is a schematic representation of a case type management userinterface 1370 of an application for capturing and/or processing adigital image comprising a digital representation of a document,according to one embodiment. As shown in FIG. 13H, case type managementuser interface 1370 includes one or more interactive buttons 1372, oneor more fields 1374, and a title region 1376.

In one embodiment, interactive buttons 1372 may facilitate a usernavigating between case type management user interface 1370 and anyother user interface described herein, as well as cancel/undo a casetype management operation.

In preferred embodiments, a user interacting with case type managementinterface 1370 may set and/or change a case type associated with an opencase by interacting with one of the fields 1374, each field preferablycorresponding to a particular case type with which case type-specificdata may be associated, e.g. one or more document types, sizes, aspectratios, case information such as a customer name, a date, a location, aloan number, etc. as would be understood by one having ordinary skill inthe art upon reading the present descriptions.

Title region 1376 may display indicia that a user is being presented anedit object action user interface 1370, information about the case, etc.as would be understood by one having ordinary skill in the art uponreading the present descriptions.

FIG. 13I is a schematic representation of an enter case data userinterface 1380 of an application for capturing and/or processing adigital image comprising a digital representation of a document,according to one embodiment. As shown in FIG. 13I, enter case data userinterface includes a title region 1389, a plurality of interactivebuttons 1382, a plurality of case data fields 1384, and a user inputinterface 1386 comprising a plurality of input keys 1388. In preferredembodiments, the user input interface 1386 may substantially representany of a number of standard user input interfaces, such as a keyboard.

Interactive buttons 1382 may facilitate a user navigating between entercase data user interface 1380 and any other user interface describedherein, as well as save, cancel, undo, etc. a case data entry operation.

In one embodiment, a user may be presented with the user input interface1386 upon interacting with one of the case data fields 1384 in order tofacilitate the user entering case data into the case data field 1384.

Title region 1389 may display indicia that a user is being presented anedit object action user interface 1380, information about the case, etc.as would be understood by one having ordinary skill in the art uponreading the present descriptions.

FIG. 13J is a schematic representation of a capture signature userinterface 1390 of an application for capturing and/or processing adigital image comprising a digital representation of a document,according to one embodiment. As shown in FIG. 13J, capture signatureuser interface includes a plurality of interactive buttons 1392, asignature capture region 1394, a case data display region 1396, and atitle region 1398.

Interactive buttons 1392 may facilitate a user navigating betweencapture signature user interface 1390 and any other user interfacedescribed herein, as well as saving, canceling, undoing, etc. asignature capture operation.

Signature capture region 1394 may facilitate a user capturing asignature, e.g. a handwritten signature using a stylus, attaching and/oruploading an electronic signature or digital signature, etc., or via anyother known method of capturing signature data, as would be understoodby one having ordinary skill in the art upon reading the presentdescriptions.

Case data display region 1396 may, in some approaches, be configured todisplay case data such as may be entered by a user into one of the casedata fields 1384 utilizing case data entry user interface 1380substantially as described above with reference to FIG. 13I. Of course,any type of case data may be displayed in case data display region 1396.

Title region 1398 may display indicia that a user is being presented anedit object action user interface 1390, information about the case, etc.as would be understood by one having ordinary skill in the art uponreading the present descriptions.

FIG. 13K is a schematic representation of a submit case user interface13000 of an application for capturing and/or processing a digital imagecomprising a digital representation of a document, according to oneembodiment. As shown in FIG. 13K, submit case user interface 13000includes a plurality of interactive buttons 13002, a progress bar 13004,and one or more case data display regions 13006.

Interactive buttons 1392 may facilitate a user navigating betweencapture signature user interface 1390 and any other user interfacedescribed herein, as well as submitting, deleting, etc. a case.

In one approach, progress bar 13004 may provide progress informationregarding submitting a case, e.g. to a remote host over a network. Inpreferred approaches, progress bar 13004 provides visual indicia ofsubmission progress via the progress bar 13004 changing appearance (e.g.by filling with a particular color, such as green, yellow, red, etc.) ina left-to-right direction. Of course, other methods of indicatingsubmission progress (e.g. by displaying a percentage completion, a dataupload rate and/or amount of uploaded data, etc.) may also be employedwithout departing from the scope of the present disclosures.

In more embodiments, case data display region 13006 may, in someapproaches, be configured to display case data such as may be entered bya user into one of the case data fields 1384 utilizing case data entryuser interface 1380 substantially as described above with reference toFIG. 13I. Of course, any type of case data may be displayed in case datadisplay region 1396.

In several approaches, it may be advantageous to facilitate outputtingdata relating to one or more cases for creating physical representationsof case data, images relating to cases, etc. In one exemplary approach,the mobile software application may include one or more interfacesconfigured to facilitate printing case information and associatedimages, such as described below with reference to FIGS. 14A-14D.

FIG. 14A is a schematic representation of a print case user interface1400 of an application for capturing and/or processing a digital imagecomprising a digital representation of a document, according to oneembodiment. As shown in FIG. 14A, print case user interface 1400 mayinclude a plurality of interactive buttons 1402, a plurality ofdescriptive fields 1404, each field optionally including a selectioninterface 1406, and a title region 1408.

In one embodiment, interactive buttons 1402 may facilitate one or moreuser actions relating to printing a case, images or other data relatingto a case, etc. For example, interactive buttons 1402 may facilitateuser navigation between print case user interface 1400 and any otheruser interface described herein, may facilitate a user searching forprinters, e.g. by geographic location, using an electronic mappingsystem, by prior use (e.g. searching through a print history), mayfacilitate a user modifying one or more print settings, may facilitate auser viewing printers in conjunction with a map, etc. as would beunderstood by one having ordinary skill in the art upon reading thepresent descriptions.

In further embodiments, descriptive fields 1404 may include informationdescribing one or more printers, print locations, etc., such as ageographic location of a printer (e.g. an address, a distance from themobile device, etc.), a name of an entity controlling the printer, e.g.“FedEx,” “AIM Mail Centers,” “Doubletree Hotel,” “Kinkos,” etc., and/orany other identifying information useful to facilitate a user locating aprinter for use in printing information relating to a case. In preferredembodiments, some or all of the data displayed in descriptive fields1404 may be retrieved and/or organized according to a user-submittedsearch query (not shown) which may be utilized to locate informationrelating to one or more printers using electronic resources such as asearch engine, relational database, electronic service listing, etc. aswould be understood by one having ordinary skill in the art upon readingthe present descriptions.

In several embodiments, one or more descriptive fields 1404 may furtherinclude a selection interface 1406 configured to facilitate a userselecting a print resource associated with information displayed in therespective descriptive field 1404. Upon interacting with the selectioninterface 1406, print case user interface may display additional detailsand/or interfaces regarding the associated print resource, and/or mayfacilitate a user submitting a print job to the print source, in variousapproaches. Several exemplary embodiments of additional details and/orinterfaces accessible via the selection interface are described belowwith reference to FIGS. 14B-14D.

In still more embodiments, title region 1408 may display indicia that auser is being presented print case user interface 1400, informationabout the case, etc. as would be understood by one having ordinary skillin the art upon reading the present descriptions.

FIG. 14B is a schematic representation of a select printer userinterface 1410 of an application for capturing and/or processing adigital image comprising a digital representation of a document,according to one embodiment. As shown in FIG. 14B, select printer userinterface 1410 may include a plurality of interactive buttons 1412, asearch query field 1414, a printer data field 1416, a printer resourceshortcut interface 1418, and a title region 1419.

In one embodiment, interactive buttons 1402 may facilitate one or moreuser actions relating to printing a case, images or other data relatingto a case, etc. For example, interactive buttons 1412 may facilitateuser navigation between select printer user interface 1410 and any otheruser interface described herein, may facilitate a user searching forprinters, e.g. by geographic location, using an electronic mappingsystem, by prior use (e.g. searching through a print history), mayfacilitate a user modifying one or more print settings, may facilitate auser viewing print resources in conjunction with a map, particularlyprint resources within a predetermined distance of the mobile device,etc. as would be understood by one having ordinary skill in the art uponreading the present descriptions.

In several embodiments, search query field 1414 may be configured toaccept input from a user and facilitate locating print resources througha search process. The search process may optionally locate informationrelating to one or more printers using electronic resources such as asearch engine, relational database, electronic service listing, etc. aswould be understood by one having ordinary skill in the art upon readingthe present descriptions. Any known search method may be used inconnection with the search query field, and searches may be performedusing any suitable form of input, particularly alphanumeric inputreceived from a user.

In more embodiments, printer data field 1416 may display data associatedwith one or more print resources, such as a geographic location, networkaddress, printer description, etc. Moreover, printer data field 1416 mayfacilitate displaying one or more print resources having been previouslyselected by a user, and/or print resources having been previouslydesignated as belonging to a particular print resource classification bythe user, for example, “favorite” print resources, “last used”resources, etc. as would be understood by one having ordinary skill inthe art upon reading the present descriptions.

In still more embodiments, title region 1419 may display indicia that auser is being presented select printer user interface 1410, informationabout the case, etc. as would be understood by one having ordinary skillin the art upon reading the present descriptions.

FIG. 14C is a schematic representation of a print details user interface1420 of an application for capturing and/or processing a digital imagecomprising a digital representation of a document, according to oneembodiment. As shown in FIG. 14C, print details user interface 1420includes a plurality of interactive buttons 1422, a plurality of printdata fields 1424, each print data field 1424 being characterized by oneor more of a selection interface 1426 and/or a flagging interface 1428,and a title region 1429.

In one embodiment, the plurality of interactive buttons 1422 mayfacilitate user navigation between print details user interface 1420 andany other user interface described herein, may facilitate a usersearching for printers, e.g. by geographic location, using an electronicmapping system, by prior use (e.g. searching through a print history),may facilitate a user modifying one or more print settings, etc. aswould be understood by one having ordinary skill in the art upon readingthe present descriptions.

In several embodiments, print data fields 1424 may display one or moredetails regarding a printing task, e.g. data associated with an image tobe printed, a print location, etc. Moreover, each data field 1424 mayoptionally comprise one or more of a selection interface 1426 and/or aflagging interface 1428. The selection interface 1426 may facilitate auser selecting data displayed in data field 1424 for further review,e.g. in order to view an image having data associated therewithdisplayed in data field 1424. Similarly, flagging interface 1428 mayfacilitate a user designating data displayed in a data field 1424 asbelonging to or being associated with data of a particularclassification, e.g. by designating a particular print resource, printlocation, etc. as a “favorite” resource, location, etc. in variousembodiments.

In still more embodiments, title region 1429 may display indicia that auser is being presented select printer user interface 1420, informationabout the case, etc. as would be understood by one having ordinary skillin the art upon reading the present descriptions.

FIG. 14D is a schematic representation of a print job user interface1430 of an application for capturing and/or processing a digital imagecomprising a digital representation of a document, according to oneembodiment. As shown in FIG. 14D, print job user interface includes aplurality of interactive buttons 1432, a print job data field 1434, aprint job progress indicator 1436, a print job success indicator 1438,and a title region 1439.

In one embodiment, the plurality of interactive buttons 1432 mayfacilitate user navigation between print details user interface 1430 andany other user interface described herein, may facilitate a usersearching for printers, e.g. by geographic location, using an electronicmapping system, by prior use (e.g. searching through a print history),may facilitate a user modifying one or more print settings, etc. aswould be understood by one having ordinary skill in the art upon readingthe present descriptions.

In more embodiments, print job data field 1434 may display data relatingto a print job, e.g. a print job location, print job resource, print jobsubmission status, print job submission date/time, print job duration,print job size (file size, number of pages, etc.), and/or other printjob data as would be understood by one having ordinary skill in the artupon reading the present descriptions. In some approaches, print jobdata field may include a print job progress indicator 1436 such as aprogress bar, which may be substantially similar in appearance andfunction to progress bar 13004 as described above with reference to FIG.13K. Of course other progress indicators are within the scope of thepresent disclosures, including progress indicators such as displaying aprint job percent completion, a print job time remaining, a print jobdata transfer, etc. as would be understood by one having ordinary skillin the art upon reading the present descriptions.

In preferred embodiments, print job user interface 1430 may also includea print job success indicator 1438, which may take any known form, suchas displaying an icon, a symbol, alphanumeric text, etc. indicating aprint job has been successfully uploaded to the print resource, physicalrepresentations of the digitally submitted print job have beensuccessfully generated, etc. as would be understood by one havingordinary skill in the art.

In still more embodiments, title region 1439 may display indicia that auser is being presented select printer user interface 1430, informationabout the case, etc. as would be understood by one having ordinary skillin the art upon reading the present descriptions.

Turning now to FIGS. 15A-16D, several exemplary embodiments of an imagecapture user interface are depicted. The exemplary embodiments shown inFIGS. 15A-16D merely represent potential configurations for such animage capture user interface within the scope of the presentdisclosures, and any known image capture interface or methodology may beutilized in conjunction with the mobile image capture and processingalgorithms and/or applications described herein.

FIG. 15A is a schematic representation of an image capture userinterface 1500 of an application for capturing and/or processing adigital image comprising a digital representation of a document,according to one embodiment. As shown in FIG. 15A, image capture userinterface 1500 includes a plurality of interactive buttons 1502configured to facilitate a user performing one or more image captureactions, such as adjusting a flash mode (e.g. selecting one ofauto-flash, flash-on, and flash off, etc.), capturing content,navigating between the image capture user interface 1500 and any otherinterface described herein, associating one or more of image data, videodata, and/or audio data with a captured image, etc. as would beunderstood by one having ordinary skill in the art upon reading thepresent descriptions.

FIG. 15B is a schematic representation of another image capture userinterface 1510 of an application for capturing and/or processing adigital image comprising a digital representation of a document,according to one embodiment. Similar to the image capture user interface1500 described above, the image capture user interface 1510 shown inFIG. 1510 also includes a plurality of interactive buttons 1502 withsubstantially identical functionality as discussed above regarding FIG.15A. In addition, image capture user interface 1510 may be furtherconfigured to assist a user in capturing a high-quality digital imagefor efficient, accurate processing utilizing one or more algorithmicprocessing operations described herein.

For example, in one embodiment image capture user interface 1510 may beconfigured to receive input from a user, e.g. via one of the interactivebuttons 1502 directing a capture component of the mobile device tocapture an image. Upon receiving such input, the image capture userinterface 1510 may display an image capture assistance message, e.g. ina status message region 1512, instructing the user to take one or moreactions to facilitate capturing a high-quality image. In preferredembodiments, the mobile application may be in communication with one ormore of an accelerometer and/or a gyroscope integrated with the mobiledevice, and may receive stability data indicating an amount and/ordirection of mobile device movement. Such stability data may be receivedfrom the accelerometer, the gyroscope, or both, in various embodiments.

Upon detecting the amount and/or direction of mobile device movement,the status message region 1512 may instruct a user to hold the mobiledevice still, to place the mobile device on a flat surface, or takeother action to facilitate capturing a high-quality image, as would beunderstood by one having ordinary skill in the art upon reading thepresent descriptions. In preferred embodiments, upon detecting that theamount and/or direction of mobile device movement is less than apredetermined threshold, the status message region 1512 may displayanother message indicating that image capture is in progress.

FIG. 15C is a schematic representation of an image capture result userinterface 1520 of an application for capturing and/or processing adigital image comprising a digital representation of a document,according to one embodiment. As shown in FIG. 15C, image capture resultuser interface includes a plurality of interactive buttons 1522, acaptured image comprising a digital representation of a document 1524,and a status message region 1526.

In one embodiment, the plurality of interactive buttons 1522 may beconfigured to facilitate a user performing one or more image reviewactions and/or one or more image capture actions, such capturingcontent, re-capturing content, and/or navigating between the imagecapture result user interface 1520 and any other interface describedherein, associating one or more of image data, video data, and/or audiodata with a captured image, etc. as would be understood by one havingordinary skill in the art upon reading the present descriptions.

In some embodiments, upon capturing content such as an image, imagecapture result user interface 1522 may be output to a display of themobile device, and information relating to the captured content may bedisplayed in status message region 1526. In preferred embodiments,information relating to the captured content may particularly relate tothe quality of the captured content, e.g. whether a digitalrepresentation of a document was detected in the image, whether theimage is characterized by one or more blurred regions, whether anillumination characteristic of the image is acceptable, etc. as would beunderstood by one having ordinary skill in the art upon reading thepresent descriptions. Of course, other information, whether relating toimage quality or otherwise, may additionally and/or alternatively bedisplayed in status message region 1526 without departing from the scopeof the present disclosures.

FIG. 16A is a schematic representation of a capture image attachmentuser interface 1600 of an application for capturing and/or processing adigital image comprising a digital representation of a document,according to one embodiment. As shown in FIG. 16A, capture imageattachment user interface 1600 includes a plurality of interactivebuttons 1602 and a plurality of horizontal and/or vertical gridlines1604.

In various approaches, the plurality of interactive buttons 1602 may beconfigured to facilitate a user performing one or more image attachmentcapture actions such capturing content, saving captured content,recapturing content, adjusting image capture settings such as flash,zoom, brightness, color/greyscale, etc. as well as facilitating a usernavigating between the image attachment capture result user interface1600 and any other interface described herein, etc. as would beunderstood by one having ordinary skill in the art upon reading thepresent descriptions.

As will be appreciated by one having ordinary skill in the art uponreading the present descriptions, embodiments of capture imageattachment user interface 1600 including a plurality of gridlines 1604may assist a user in aligning a capture field of the capture componentwith target content, e.g. with borders of a document. In variousembodiments, a user may be able to toggle the presence of visiblegridlines 1604 on the mobile display when interacting with captureattachment user interface 1600, may be able to customize the number anddirection of gridlines (e.g. horizontal, vertical, both or neither) andmay be able to determine the position of gridlines 1604 such that, forexample, a central region within the grid corresponds to a predeterminedset of known dimensions. In preferred embodiments, the region within thegrid corresponding to the predetermined set of known dimensions maycorrespond to dimensions of a known document type, which may include anytype described herein as well as others that would be appreciated by askilled artisan reading the present descriptions.

In additional and/or alternative embodiments, using capture imageattachment user interface 1600, a user may capture either an imagestored on a storage medium integrated into or coupled to the mobiledevice, capture an image using a capture component of the mobile device,or both. Moreover, a result depicting the captured, recaptured, etc.content may be outputted to a display of the mobile device uponreceiving user input instructing the capture component of the mobiledevice to capture content and capturing such content using the mobilecapture component. In preferred embodiments, displaying the result inthis manner facilitates a user reviewing the captured content and mayoptionally facilitate a user editing the captured content according toany methodology or operation described herein.

FIG. 16B is a schematic representation of a capture audio attachmentuser interface 1610 of an application for capturing and/or processing adigital image comprising a digital representation of a document,according to one embodiment. As shown in FIG. 16B, the capture audioattachment user interface 1610 includes a plurality of interactivebuttons 1612, a plurality of data fields 1614, and a playback progressindicator 1616.

In various embodiments, the plurality of interactive buttons 1612 may beconfigured to facilitate a user performing one or more audio contentcapture and/or review actions, such capturing audio content, reviewing(e.g. playing back) audio content, deleting captured audio content,associating audio data with captured image and/or video content, and/ornavigating between the capture audio attachment user interface 1620 andany other interface described herein, etc. as would be understood by onehaving ordinary skill in the art upon reading the present descriptions.In some approaches, a user may be facilitated in reviewing audio contentby interacting with playback progress indicator 1616 using any knownmethod.

In addition, data fields 1614 may be configured to display one or moreaudio files and/or metadata associated with one or more audio files. Instill more embodiments, data fields 1614 may similarly display one ormore video files and/or metadata associated with the one or more videofiles. For example, such metadata may include a capture date and/ortime, an audio clip length, a name associated with an audio or videoclip, information relating to a case associated with the audio or videoclip, etc. as would be understood by one having ordinary skill in theart upon reading the present descriptions.

FIG. 16C is a schematic representation of a capture video attachmentuser interface 1620 of an application for capturing and/or processing adigital image comprising a digital representation of a document,according to one embodiment. As shown in FIG. 16C, capture videoattachment user interface includes a plurality of interactive buttons1622, a video display region 1624, a playback progress indicator 1626and a title region 1628.

In various embodiments, the plurality of interactive buttons 1612 may beconfigured to facilitate a user performing one or more audio contentcapture and/or review actions, such capturing video content, reviewing(e.g. playing back) video content in video display region 1624, deletingcaptured video content, associating video content with captured imagecontent and/or metadata, and/or navigating between the capture videoattachment user interface 1620 and any other interface described herein,etc. as would be understood by one having ordinary skill in the art uponreading the present descriptions. In some approaches, a user may befacilitated in reviewing video content by interacting with playbackprogress indicator 1626 using any known method.

In more embodiments, title region 1628 may display indicia that a useris being presented a capture video attachment user interface 1620,information about the case, etc. as would be understood by one havingordinary skill in the art upon reading the present descriptions.

In various embodiments, the plurality of interactive buttons 1622 may beconfigured to facilitate a user performing one or more audio contentcapture and/or review actions, such capturing video content, reviewing(e.g. playing back) video content in video display region 1624, deletingcaptured video content, associating video content with captured imagecontent and/or metadata, and/or navigating between the capture videoattachment user interface 1620 and any other interface described herein,etc. as would be understood by one having ordinary skill in the art uponreading the present descriptions. In some approaches, a user may befacilitated in reviewing video content by interacting with playbackprogress indicator 1626 using any known method.

FIG. 16D is a schematic representation of a mobile scanner image captureuser interface 1630 of an application for capturing and/or processing adigital image comprising a digital representation of a document,according to one embodiment. As shown in FIG. 16D, mobile scanner imagecapture user interface 1630 includes a plurality of interactive buttons1632, a display region configured to receive and/or display a digitalrepresentation of a document 1634, and a status message region 1636.

In various embodiments, the plurality of interactive buttons 1632 may beconfigured to facilitate a user wirelessly connecting to a mobilescanning device (not shown), communicating one or more commands to themobile scanning device, receiving data from the mobile scanning device,and/or navigating between the capture video attachment user interface1630 and any other interface described herein, etc. as would beunderstood by one having ordinary skill in the art upon reading thepresent descriptions.

While the presently described processing operations and user interfacesare particularly capable of processing images captured using a mobilecapture component (e.g. camera) of a mobile device that includeartifacts and present challenges not encountered when analyzing imagescaptured using traditional flatbed scanners, multifunction devices,etc., the instant processing operations and user interfaces are fullysuited for processing images captured from such traditional scanners,etc. Thus, the presently disclosed embodiments offer a robust,capture-platform-independent analytical system and method for processingdigital images.

For example, in some approaches, a user may initiate a connection with amobile scanning device, and may receive image data from the mobilescanning device upon communicating a transmit data command from themobile device to the mobile scanner. In preferred approaches, the mobilescanner may then transmit data to the mobile device. Transmitted datamay be either data stored on a storage medium integrated into or coupledto the mobile scanner, or data captured using a capture component of themobile scanner. For example, upon receiving a transmit data command fromthe mobile device, the mobile scanner may scan one or more documents,e.g. documents positioned in an automatic document feeder (ADF) of themobile scanner.

Status message region 1636 may be configured to display one or moremessages pertaining to the capture, transmission, receipt, and/orprocessing of data via the mobile scanner. Messages of any known typemay be displayed using any known methodology.

FIG. 17 is a schematic representation of a settings user interface 1700of an application for capturing and/or processing a digital imagecomprising a digital representation of a document, according to oneembodiment. As shown in FIG. 17A, settings user interface 1700 includesa navigation button 1702, and a plurality of settings fields 1704, eachsettings field 1704 optionally including one or more of a selectioninterface 1706 and a toggle interface 1708, and a title region 1710.

In one embodiment, navigation button 1702 may be configured tofacilitate a user navigating between the settings user interface 1700and any other interface described herein, etc. as would be understood byone having ordinary skill in the art upon reading the presentdescriptions.

Settings fields 1704 may be configured to facilitate a user defining,modifying, synchronizing, and/or resetting one or more settings relatingto any navigation, case management, interface organization, interfaceappearance, mobile device communication, image capture, imageprocessing, etc. functionality as described herein and as would furtherbe understood by one having ordinary skill in the art upon reading thepresent descriptions.

In various approaches, one or more settings may be configured via a userinteracting with a selection interface 1706 configured to direct a userto a more detailed interface for modifying options relating to thesetting, e.g. facilitating the user to select one from among severalpossible settings displayed in a drop-down list, entering data ormetadata in a data entry field, etc. as would be understood by onehaving ordinary skill in the art upon reading the present descriptions.

Additionally and/or alternatively, one or more settings may beconfigured via a user interacting with a toggle interface 1708 that maybe alternately set to one of two possible states for a given setting.Toggle interface 1708 may be particularly preferable for settings havingonly two possible states, such as enabling/disabling functionalities,switching between one of two alternate configurations, etc. as would beunderstood by one having ordinary skill in the art upon reading thepresent descriptions.

Specific, but nonlimiting examples of settings capable of beingmanipulated via settings interface 1710 include virtual renderingsettings such as toggling real-time analysis feedback, processing mode(full, preview, etc.), display preferences for captured content(original format, enhanced, black and white, greyscale, etc.), togglinggeographic tagging of images, configuring connection and/orauthentication information, configuring status message display settings,determining image processing and/or analysis scheduling (e g immediatelyupon capture, at a user-defined date and time, upon reviewing a case,etc.) processing location (e.g. client device, host device, cloudresources, etc.), toggling image capture stability assistance,notification settings, and/or other settings relevant to anyfunctionality described herein, as would be understood by one havingordinary skill in the art upon reading the present descriptions.

Title region 1710 may display indicia that a user is being presented theselect settings user interface 1700, etc. as would be understood by onehaving ordinary skill in the art upon reading the present descriptions.

The application described herein for use in capturing and/or processingdigital images comprising one or more digital representations of adocument may further include notification functionalities capable ofinforming a user about any relevant information pertaining to imageprocessing, case management, etc. according to various embodiments. Inpreferred approaches, users may further be facilitated in receivingdesirable notifications, not receiving undesired notifications, and/orconfiguring notification characteristics via a notifications userinterface, such as the exemplary embodiment described below withreference to FIG. 18.

FIG. 18 is a schematic representation of a notifications user interface1800 of an application for capturing and/or processing a digital imagecomprising a digital representation of a document, according to oneembodiment. As shown in FIG. 18, notifications interface 1800 includes anavigation button 1802, a plurality of notification fields 1804, eachnotification field optionally including a selection interface 1806, atoggle interface 1808 and a title region 1810.

Generally, in various approaches a user may be facilitated in selecting,creating, modifying and/or deleting notifications by interacting withone or more notification fields 1804, e.g. via a corresponding selectioninterface 1806. As will be understood by one having ordinary skill inthe art reading the present descriptions notifications may take anyknown format and may utilize one or more functionalities native to themobile device operating system. For example, in various embodimentsnotifications may comprise one or more of an email, a text message, apop-up alert, a banner, a meeting or other event managed using acalendar interface, etc.

In further embodiments, notifications user interface 1800 may facilitatea user reviewing recent notifications, configure notification repetitionfrequency, determine whether to display notifications when the mobiledevice is locked, whether to display notifications when a mobile devicedisplay is off or in a “sleep mode,” etc.

Additionally and/or alternatively, one or more settings may beconfigured via a user interacting with a toggle interface 1818 that maybe alternately set to one of two possible states for a given setting.Toggle interfaces may be particularly preferable for configuringnotifications having only two possible states, such asenabling/disabling notifications.

Title region 1808 may display indicia that a user is being presented thenotifications user interface 1800, etc. as would be understood by onehaving ordinary skill in the art upon reading the present descriptions.

Use Methodology

In one illustrative use, the document(s) being photographed can bevalidated. For example, assume the document(s) is a hotel receiptsubmitted with an expense report. Then the application and/or the servercan check whether the receipt is actually for that user. If the hotelthat issued the receipt has put a different name on the receipt, awarning may be output, requesting verification that the invoice is forthat user. As discussed in more detail below, the mobile device may sendsome identifying information that corresponds to the device and/or user,thereby enabling such validation.

In another illustrative use, assume an employee wants to cheat on anexpense report. The employee borrows receipts from a friend and takespictures of those in order to inflate reimbursable expenses. As above,names, credit card numbers, etc. can be cross-checked.

Illustrative methods of document validation that may be used in variousembodiments of the present invention are disclosed in U.S. patentapplication Ser. No. 12/368,685, filed Feb. 10, 2009 and entitled“Systems, Methods, and computer program products for determiningdocument(s) validity” and which is herein incorporated by reference. Anadvantage of some approaches disclosed therein is that the systemalready knows what it is expecting, so upon receipt of an invoice, andwith the purchase order number already known, the system easilydetermines what should have been invoiced. Thus, the system can moreeasily extract that data from the document(s) and more easily check it.In the present scenario, the system knows what entity would beperforming the back extraction, taking the image, and for what kind ofbusiness process this image is intended. Accordingly, the system mayexpect an image that has made it to this business process, and so theaccuracy with which the algorithm is capable of extracting data from theimage drastically improves.

In one embodiment, a portable scanner may communicate with the mobiledevice using any known connection, e.g., using Bluetooth, WiFi, a USBcable, etc. and instead of taking the picture with the camera, theportable scanner scans the document(s) and transmits the document(s) tothe mobile device. Upon receipt, the image data may be processed asdiscussed herein.

According to one embodiment, the architecture may incorporate a mobiledevice having a mobile application thereon, and one or more remoteservers, which may be standalone, part of a cloud service, etc. Any ofthese may incorporate Kofax Capture, the Kofax transformation modules,and/or other software available from Kofax Inc., 15211 Laguna CanyonRoad, Irvine, Calif. 92618-3146, United States.

In various approaches, a mobile device and/or a remote server mayinitiate a login authentication. In such case, a login authenticationmay be initiated automatically once the mobile application is opened,upon receiving a request from a user, an upload from the mobile deviceto the remote server is initiated, etc. or any of the other approachesdescribed above. Examples of embodiments including login authenticationsare presented below.

According to one approach, when a mobile device receives a request froma user to login to the remote server, a signal may be sent to the cloudservice. In various approaches, the cloud service may be hosted byKofax, a third party, the user, etc. This cloud service may then assistin connecting the mobile device to a corresponding remote server.

In one approach, software including, but not limited to Kofax FrontOffice Server (KFS) may act as a plug in, allowing the remote server toconnect to the mobile device. In another approach, KFS may also allowadministrators, programmers, etc. to create users types and/or decidewhat capabilities may be available to certain mobile devices. In oneapproach, mobile devices with similar capabilities and/or user types maybe assigned to groups. These groups may be distinguishable based onlogin information provided, a predetermined list, information receivedabout the user, etc.

In one approach, after the mobile device is connected to the remoteserver, the remote server may connect to an additional enterpriseresource planning (ERP) system, customer relationship management (CRM)system, database, remote server, etc.

According to one approach, once the login to the mobile device and/orthe remote server has been completed, the user may be able to performany number of certain predetermined tasks. These tasks may be determinedby the login information provided, a predetermined list, informationreceived about the user, saved user preferences, KFS, etc.

In one approach, based on the task request received by the mobile deviceand/or the remote server, the user may be able to create any number ofpredetermined document(s) types. In this case, the mobile device and/orremote server may receive some information from the user regarding thetype of documents which will be processed e.g., invoices, loanapplications, etc. The mobile device may receive this information fromthe user via a graphical user interface, saved user preferences, logininformation, etc. or any other method mentioned herein.

In one approach, a mobile device and/or a remote server may receivepreferences from a user regarding tasks and their association with agiven document(s) type. In another approach, these preferences may besaved and used in future embodiments. Thus, one or more document typesmay define what kind of image processing will be available,automatically performed, forbidden, etc.

In another approach, the mobile device and/or a remote server mayreceive input from the user regarding the desired information to beextracted from a specific document(s). This input may then be saved onthe mobile device and/or the remote server for future use. The desiredinformation may require processing that may be the same as, similar to,or different than the aforementioned preferences. Alternatively, theinput regarding the information to be extracted from a specificdocument(s) or document(s) type may be received from the cloud serviceand/or the remote server(s).

Similarly, in still another approach mobile devices may receive inputfrom a user regarding which jobs should be processed on the desiredinformation for a specific document(s), document(s) type, etc. Inputregarding job-processing preferences may be saved on the mobile deviceand/or the remote server as well. Documents and/or preferences unique tothose documents may be distinguished from others by using file names,security passcodes, etc.

Using the capture software described above, the camera of a mobiledevice may capture any number of images and/or audio recordings.

In one approach, the image processing described above may initiate a jobon one or more objects, which may be processed on the mobile deviceand/or the remote server.

In another approach, the job may be processed on the mobile device ifthe processing speed, available memory, battery life, etc. meet somethreshold predetermined by the user, job, remote server, cloud service,etc. Moreover, it is preferable that if that threshold is not met, thejob is processed partially or entirely on one or more remote serverswithin a cloud service to optimize performance. In one approach, it maybe predetermined by the user, cloud service, remote server, etc. for anyone or more jobs to be processed solely on the mobile device and/or theremote server regardless of any thresholds.

In one methodology, if it is desired, the mobile device may receive asignature from a user via a touchpad, a stylus, an audio recording, etc.which may provide authentication to documents being sent from the mobiledevice to a destination. In one approach, one or more signatures may beadded to a case (e.g., explained below) or sent alone from a mobiledevice.

Once the desired jobs have been completed, the mobile device may receivea request to send one or more documents to the remote server. Softwareincluding, but not limited to Kofax Transformation Module (KTM) may actas a plugin to Kofax Capture (KC) software and may assist with the dataextraction.

These documents may preferably be packaged and sent together as a case,but may be sent individually as well. When sending documents from amobile device, identification such as a confirmation number, claimnumber, verification code, etc. may be included to help index thedocuments. In one approach, the mobile device may receive theidentification from the user.

Alternatively, the documents may be sent without identification so thatthe cloud service, remote server, etc. may autonomously index documents.In one approach, the documents may be sent to the cloud service, beforearriving at the remote server. In this event, the documents may beclassified by the remote server based on the available information fromthe login authentication, saved user preferences, document(s) types,image processing, etc. Once processed, the remote server may transmitthese documents to another server, a third party, etc.

In one approach, the document(s) types of some or all captured documentsare determined automatically. In one approach, raw image information isused to determine the type of one or more documents. In anotherapproach, any number of image processing operations as described hereinis performed before the image information is used to determine the typeof one or more documents. In yet another approach, information derivedfrom an image (e.g. the text derived from an image by use of OCR, a barcode found in the image, etc.) is used to determine the type of one ormore documents. Where such determination is performed (locally orremotely) may be configured as described herein. Any knownclassification method may be used to determine the document(s) type, forexample those described in U.S. Pat. Nos. 7,386,527 and 7,761,391, whichare herein incorporated by reference.

Preferably, the automatic determination is output to the user, who canverify the type of the document(s), and optionally change the type ofdocument(s). In another approach, the automatic determination is onlyoutput to the user if the confidence of the determination is below acertain threshold.

The document(s) type may be stored; transmitted e.g., in conjunctionwith the image of the document(s); be used to determine a recipient ofthe document(s) and/or initiate a business process; etc. Techniques thatmay be adapted for such purposes include those described in U.S. patentapplication Ser. No. 11/743,110, filed May 1, 2007, entitled “Systemsand methods for routing facsimiles based on content” and which is hereinincorporated by reference.

In one approach, prior to sending the documents from the mobile device,the remote server and/or the mobile device may perform some review todetermine if any additional documents, signatures, pictures, audiorecordings, etc. are required. In various approaches, the reviewparameters may be preset by the user, preset by the application,dependent upon login information, etc. Examples of reviews are presentedin more detail below.

In one approach, the user and the mobile device and the cloud serviceand/or remote server(s) operate in an interactive fashion to validateinformation extracted from an image, e.g., using methodology such asthat disclosed in U.S. patent application Ser. No. 12/368,685, asmentioned before.

In one approach, the mobile device may receive a notification such as anemail, text message, voicemail, link, etc. from the remote server inresponse to processed documents. The mobile device may provide thisnotification to the user as to inform the user how the documents wereprocessed.

The following examples are in no way meant to limit the presentinvention, but rather are intended to provide illustrative embodimentsthat place the inventive concepts in a context. One skilled in the art,upon reading the present disclosure, would understand the plethora ofpermutations of the present invention that are encompassed by thedescription provided herein.

One illustrative embodiment may include a field claims adjuster forMercury Auto Insurance. On a given day, the field claims adjuster mayvisit a particular body shop regarding a particular claim filed that dayby a client. At the body shop, the field claims adjuster (user) may takeout his mobile device and activate a Mobile Application as describedherein. Upon doing so, the mobile device may prompt the field claimsadjuster to login. After the field claims adjuster provides his logininformation, the Mobile Application may identify that he is in fact aninsurance claims adjuster.

At this point, the mobile device may inform the field claims adjusterthat he is able to process a field claim (task). Upon selecting a fieldclaim, the field claims adjuster is informed that he is able to processa repair quote, an accident report, a proof of insurance, a driverlicense, a general correspondence, and photos of the damaged vehicle(document(s) types). Therefore, these types of documents are what thefield claims adjuster may be allowed to process based on his logininformation.

The field claims adjuster may decide to use the capture softwaredescribed above and capture individual pictures of a repair quote, anaccident report, and a driver license using the camera in his mobiledevice. Whereupon, the image processing described above may process thepictures on the mobile device itself and/or the image may be pushed upto a remote server for processing.

In addition, the field claims adjuster may capture an image whilewalking around the damaged vehicle, as well as capture an audiostatement recording from the client who is submitting the claim.Furthermore, the field claims adjuster may have the same clientdigitally sign a window in his mobile device using a finger or a toolsuch as a stylus for further authorization and/or authentication.

Once this content has been collected, the field claims adjuster maycreate a case which combines all the gathered pictures, videos, audiorecordings, and digital signatures. Then the field claims adjuster maysend this case with the customer's claim number to a remote server.

In one approach, this case may be sent to a cloud system, whereupon itis indexed to the remote server corresponding to the customer'sparticular claim.

Once the case is delivered to the remote server, the relevantinformation may be extracted, and some email or SMS may be sent back tothe field claims adjuster and/or the customer who filed the claiminforming them of the status and/or providing them a link.

In another approach, the customer filing the claim may be required toperform the aforementioned steps of the present illustrative embodimentwith their own mobile device and submit the case documents to theMercury Auto Insurance company before the company is willing to send afield claims adjuster to the site of the vehicle. In another approach,the field claims adjuster may not be required to come to the site of thevehicle if the customer is able to perform any, some or all of theaforementioned embodiment themselves.

Another illustrative embodiment may include a loan officer. On aparticular day, a loan officer may visit a client who wishes torefinance their loan. Once with the client, the loan officer (user) maytake out his tablet (mobile device) and activate the Mobile Application.Upon doing so, the tablet may prompt the loan officer to login. Afterthe loan officer provides his login information, the Mobile Applicationmay identify that he is in fact a loan officer.

At this point, the tablet may inform the loan officer that he is able toprocess a field loan (task). Upon selecting a field loan, the loanofficer is informed that he is able to process property comparisons, atermite report, proof of residence, and income verification (document(s)types). Therefore, these types of documents are what may be allowed tobe submitted and/or processed based on his login information.

The loan officer may decide to use the capture software described aboveand capture individual pictures of a termite report, proof of residence,and income verification using the camera in his tablet. Thereafter, theimages may be sent to a cloud service which then selects and sends theimages to one or more remote servers to conduct the image processingdescribed above. This may be done to optimize processing time becausethe tablet itself may not have had enough processing speed, memoryand/or battery life to successfully do so.

In addition, the loan officer may capture a video while walking aroundthe client's house, as well as capture an audio statement recording fromthe client. Furthermore, the loan officer may have the same clientdigitally sign a window in his tablet using the client's finger or atool such as a stylus for further authorization.

Once this content has been collected, the loan officer may create a casewhich combines all the gathered pictures, videos, and audio recordings,but accidentally omits to include the digital signatures. Then the loanofficer then attempts to send this incomplete case with the customer'sloan number to a remote server.

However, before the case is sent to the remote server, the mobile devicemay perform a review of the components being sent in some embodiments.As explained above, based on the loan officer's login information, themobile device may know that all field loans require signatures from theclient. Therefore, when performing the review, the mobile device maydetect the absence of such signatures, thereby stopping the package frombeing sent to the remote server. In one approach, the user may beinformed of the review results by a text on a screen of the mobiledevice, an email, an audible sound projected through the speakers of themobile device, etc.

After the loan officer includes the appropriate signatures to the case,the case may be sent to a cloud system, whereupon it is indexed to theremote server corresponding to the customer's particular loan.

Once the case is delivered to the remote server, the relevantinformation may be extracted, and some email or text message may be sentback to the loan officer and/or the customer who has the loan, informingthem of the status and/or providing them a link.

In yet another illustrative embodiment, a doctor (user) may have anappointment with a patient, in which doctor may need to capture picturesof 35 separate documents from the patient. In this exemplary embodiment,for the doctor to use her mobile device to capture all 35 documentswould not be a time efficient method. Instead, the doctor may use amobile wireless scanner which wirelessly connects with the MobileApplication on the doctor's mobile device.

In another approach, the mobile scanner may connect to the mobile devicevia a wire, a cable, a removable memory chip (e.g., USB), etc.

In one approach, the mobile device may send a signal to the mobilewireless scanner upon receiving an initiation from the user. In anotherapproach, the mobile wireless scanner may receive an input directly froma user. This input may include the user tapping a button or region of amobile device display, giving a vocal command, a time lapse, etc.

Upon receiving a signal, the mobile wireless scanner may begin scanningimages of the patient's document(s) and wirelessly transmitting thescanned images to the doctor's mobile device. Thereupon the MobileApplication may receive the images and process and/or forward them thesame way, or a similar way to as if the mobile device had simplycaptured photos of all the documents.

Once the scans are transferred to the mobile device, the remainder ofthe image processing, formation of the case, etc. may be done on themobile device according to any of the embodiments described herein.

Yet another illustrative embodiment may include a FedEx delivery person(user). The delivery person may show up to a home to pick up a packagethat is being sent overseas or to a domestic location. However, forpackages being sent overseas, customs requires that the client sendingthis package declare its contents on a form.

Upon arrival, the FedEx delivery person may take out a mobile device andactivate the Mobile Application. Upon doing so, the mobile device mayprompt the FedEx delivery person to login. After the FedEx deliveryperson provides login information, the Mobile Application may identifythat he is in fact a FedEx delivery person.

At this point, the mobile device may inform the FedEx delivery person ofthe ability to process a customs submittal (task). Upon selecting acustoms submittal, the FedEx delivery person is informed of the abilityto process a customs form (document(s) type). Therefore, thisdocument(s) is what the FedEx delivery person may be allowed to processbased on the login information.

The FedEx delivery person may decide to use the capture softwaredescribed above and capture individual pictures of the customs formusing the camera in the mobile device. Whereupon, the image processingdescribed above may process the pictures on the mobile device.

In addition, the FedEx delivery person may have the same clientdigitally sign a window in his mobile device using a tool such as astylus for further authorization.

Once this content has been collected, the FedEx delivery person maycreate a case which combines all the captured pictures, and digitalsignatures. Then the FedEx delivery person may upload this case with thecustomer's package number to a remote server.

As a result, this case may be sent to a cloud system, whereupon it isindexed to the remote server corresponding to the customer's particularpackage.

Once the case is delivered to the remote server, the relevantinformation may be extracted, and some email or text message may be sentback to the FedEx delivery person and/or the customer who is sending thepackage, informing them of the status and/or providing them a link.

In another embodiment, a mobile device may receive a notificationregarding further requirements for an existing transaction. In oneapproach, the notification may include a link that may be activated bydetecting the user tapping a button on the mobile device, a portion ofthe mobile device display, etc., automatically when the notification isreceived by the mobile device, etc. Once activated, the link may connectthe mobile device to a remote server via a cloud service, a directconnection, etc.

In one approach, the user, by way of the mobile device, may then bepresented with the option to download an application from a remoteserver in exchange for some form of currency, for free, goods, etc. Inthe case that the mobile device receives permission from a user todownload the application, the download may be conducted by the mobiledevice and/or the remote server depending on processing speed, availablememory, etc. as described above. Once downloaded, the application mayallow a mobile device to perform any of the embodiments described and/orsuggested herein in conjunction with a cloud service and/or a remoteserver.

In one illustrative embodiment, a student applying to a certain collegereceives an email from the college informing him that he is required toelectronically submit some additional form of identification. In thecase that the student does not have access to some type of mobiledevice, he may activate the link provided in the email from the college,where said link presents him with the option to download a MobileApplication which will allow him to submit his identification via hismobile device.

If the student chooses to download the Mobile Application at that time,then he may capture pictures, videos, signatures, etc. and successfullysubmit them to the college as requested.

In another illustrative embodiment, a business process may be initiatedupon processing the image, e.g., to create a machine-readable (e.g., anOCRed) document(s). Illustrative methodology which may be used inconjunction with such an embodiment is described in U.S. patentapplication Ser. No. 11/743,110, as mentioned above.

It will be further appreciated that embodiments of the present inventionmay be provided in the form of a service deployed on behalf of acustomer to offer service on demand.

While various embodiments have been described above, it should beunderstood that they have been presented by way of example only, and notlimitation. Thus, the breadth and scope of an embodiment of the presentinvention should not be limited by any of the above-described exemplaryembodiments, but should be defined only in accordance with the followingclaims and their equivalents.

What is claimed is:
 1. A method, comprising: dividing, using aprocessor, a tetragon comprising a digital representation of a documentin a digital image into a plurality of sections, each section comprisinga plurality of pixels; for each section: determining whether the sectioncontains one or more sharp pixel-to-pixel transitions in a firstdirection; counting a total number of first direction sharppixel-to-pixel transitions for the section (S_(S1)); determining whetherthe section contains one or more blurred pixel-to-pixel transitions inthe first direction; counting a total number of first-direction blurredpixel-to-pixel transitions for the section (S_(B1)); determining whetherthe section contains one or more sharp pixel-to-pixel transitions in asecond direction; counting a total number of second-direction sharppixel-to-pixel transitions for the section (S_(S2)); determining whetherthe section contains one or more blurred pixel-to-pixel transitions inthe second direction; counting a total number of second-directionblurred pixel-to-pixel transitions for the section (S_(B2)); determiningthe section is blank upon determining: S_(S1) is less than apredetermined sharp transition threshold, S_(B1) is less than apredetermined blurred transition threshold, S_(S2) is less than apredetermined sharp transition threshold, and S_(B2) is less than apredetermined blurred transition threshold; and determining, for allnon-blank sections, a first direction blur ratio r₁=S_(S1)/S_(B1);determining, for all non-blank sections, a second direction blur ratior₂=S_(S2)/S_(B2); determining that a non-blank section is blurred in thefirst direction upon determining that r₁ is less than a predefinedsection blur ratio threshold; and determining that a non-blank sectionis blurred in the second direction upon determining that r₂ is less thanthe predefined section blur ratio threshold; and determining that anon-blank section is bluffed upon determining one or more of: thesection is blurred in the first direction, and the section is blurred inthe section direction; and determining a total number of blurredsections; calculating an image blur ratio R comprising: the total numberblurred sections to a total number of sections; and determining thedigital image is blurred upon determining the image blur ratio isgreater than a predetermined image blur threshold.
 2. The method asrecited in claim 1, further comprising: determining, for each section adistribution of brightness values of the plurality of pixels;determining a characteristic variability v of the distribution ofbrightness values; calculating a noticeable brightness transitionthreshold η based on v; calculating a large brightness transitionthreshold μ based on η; analyzing, for each pixel within the pluralityof pixels, a directional pattern of brightness change in a windowsurrounding the pixel; and identifying one or more of: the sharppixel-to-pixel transition and the blurred pixel-to-pixel transitionsbased on the analysis.
 3. The method as recited in claim 2, furthercomprising: defining a plurality of center pixels; sequentiallyanalyzing each of the plurality of center pixels within one or moresmall windows of pixels surrounding each center pixel; identifying thesharp pixel-to-pixel transition upon determining: the large brightnesstransition exists within an immediate vicinity of one of the centerpixels; a first small brightness variation exists before the largebrightness transition; and a second small brightness variation existsafter the large brightness transition; detecting the sharppixel-to-pixel transition upon determining: the large transition existswithin one or more of the small windows, a monotonic change inbrightness exists in the large transition; and detecting the bluffedpixel-to-pixel transition upon determining: a noticeable transitionoccurs within a small window; and the monotonic change in brightnessexists in the noticeable transition.
 4. The method as recited in claim3, further comprising, for each section: counting a total number ofsharp transitions in each of one or more chosen directions; counting atotal number of blurred transitions in each chosen direction;determining that a section is blank upon determining: the total numberof sharp transitions is less than a predefined sharp transitionthreshold and the total number of blurred transitions is less than apredefined blurred transition threshold; determining the non-blanksection is blurred upon determining a section blurriness ratiocomprising the total number of sharp transitions to the total number ofblurred transitions is less than a section blur ratio threshold in atleast one of the chosen directions; and determining that the section issharp upon determining the section is neither blank nor blurred.
 5. Themethod as recited in claim 4, further comprising: determining a totalnumber of blank sections within the plurality of sections (N_(blank));determining a total number of blurred sections within the plurality ofsections (N_(blur)); determining a total number of sharp sections withinthe plurality of sections (N_(sharp)); determining a blurriness ratio(R_(B))=N_(blur)/(N_(blur)+N_(sharp)); and determining that the digitalimage is sharp if the R_(B) is less than a blurriness threshold.
 6. Themethod as recited in claim 1, wherein the processor is part of a mobiledevice, the mobile device having an integrated camera.
 7. A system,comprising: a processor configured to execute logic; logic for dividing,using a processor, a tetragon comprising a digital representation of adocument in a digital image into a plurality of sections, each sectioncomprising a plurality of pixels; logic for determining whether thesection contains one or more sharp pixel-to-pixel transitions in a firstdirection; logic for counting a total number of first direction sharppixel-to-pixel transitions for the section (S_(S1)); logic fordetermining whether the section contains one or more blurredpixel-to-pixel transitions in the first direction; logic for counting atotal number of first-direction blurred pixel-to-pixel transitions forthe section (S_(B1)); logic for determining whether the section containsone or more sharp pixel-to-pixel transitions in a second direction;logic for counting a total number of second direction sharppixel-to-pixel transitions for the section (S_(S2)); logic fordetermining whether the section contains one or more blurredpixel-to-pixel transitions in the second direction; logic for counting atotal number of second-direction blurred pixel-to-pixel transitions forthe section (S_(B2)); logic for determining the section is blank upondetermining: S_(S1) is less than a predetermined sharp transitionthreshold, S_(B1) is less than a predetermined blurred transitionthreshold, S_(S2) is less than a predetermined sharp transitionthreshold, and S_(B2) is less than a predetermined blurred transitionthreshold; and logic for determining, for all non-blank sections, afirst direction blur ratio r₁=S_(S1)/S_(B1); logic for determining, forall non-blank sections, a second direction blur ratio r₂=S_(S2)/S_(B2);logic for determining that a non-blank section is blurred in the firstdirection upon determining that r₁ is less than a predefined sectionblur ratio threshold; and logic for determining that a non-blank sectionis blurred in the second direction upon determining that r₂ is less thanthe predefined section blur ratio threshold; and logic for determiningthat a non-blank section is blurred upon determining one or more of: thesection is blurred in the first direction; and the section is blurred inthe section direction; and logic for determining a total number ofblurred sections; logic for calculating an image blur ratio Rcomprising: the total number blurred sections to a total number ofsections; and logic for determining the digital image is blurred upondetermining the image blur ratio is greater than a predetermined imageblur threshold.
 8. A computer program product comprising anon-transitory computer readable storage medium having computer readableprogram code stored thereon, the computer readable program codecomprising: computer readable program code configured to divide, using aprocessor, a tetragon comprising a digital representation of a documentin a digital image into a plurality of sections, each section comprisinga plurality of pixels computer readable program code configured todetermine whether the section contains one or more sharp pixel-to-pixeltransitions in a first direction; computer readable program codeconfigured to count a total number of first direction sharppixel-to-pixel transitions for the section (S_(S1)); computer readableprogram code configured to determine whether the section contains one ormore blurred pixel-to-pixel transitions in the first direction; computerreadable program code configured to count a total number offirst-direction blurred pixel-to-pixel transitions for the section(S_(B1)); computer readable program code configured to determine whetherthe section contains one or more sharp pixel-to-pixel transitions in asecond direction; computer readable program code configured to count atotal number of second direction sharp pixel-to-pixel transitions forthe section (S_(S2)); computer readable program code configured todetermine whether the section contains one or more blurredpixel-to-pixel transitions in the second direction; computer readableprogram code configured to count a total number of second-directionblurred pixel-to-pixel transitions for the section (S_(B1)); computerreadable program code configured to determine the section is blank upondetermining: S_(s1) is less than a predetermined sharp transitionthreshold, S_(B1) is less than a predetermined blurred transitionthreshold, S_(S2) is less than a predetermined sharp transitionthreshold, and S_(B2) is less than a predetermined blurred transitionthreshold; and computer readable program code configured to determine,for all non-blank sections, a first direction blur ratior₁=S_(S1)/S_(B1); computer readable program code configured todetermine, for all non-blank sections, a second direction blur ratior₂=S_(S2)/S_(B2); computer readable program code configured to determinethat a non-blank section is blurred in the first direction upondetermine that r₁ is less than a predefined section blur ratiothreshold; and computer readable program code configured to determinethat a non-blank section is blurred in the second direction upondetermine that r₂ is less than the predefined section blur ratiothreshold; and computer readable program code configured to determinethat a non-blank section is blurred upon determine one or more of: thesection is blurred in the first direction; and the section is blurred inthe section direction; and computer readable program code configured todetermine a total number of blurred sections; computer readable programcode configured to calculate an image blur ratio R comprising: the totalnumber blurred sections to a total number of sections; and computerreadable program code configured to determine the digital image isblurred upon determine the image blur ratio is greater than apredetermined image blur threshold.